chore(format): formatting code

* Add Foxglove display mode for teleoperate

Add a --display_mode flag (rerun|foxglove) to lerobot-teleoperate. When set
to foxglove, stream observations/actions over a Foxglove WebSocket server:
images as RawImage/CompressedImage, scalars as typed JSON channels with
schemas generated from the feature names (sanitized so paths don't need
quoting). Adds a `foxglove` extra.

* Add Foxglove display mode to lerobot-record

Wire the --display_mode flag (rerun|foxglove) into lerobot-record, matching
lerobot-teleoperate: route init/log through the backend-agnostic dispatchers
and stop the visualization backend on exit.

* update foxglove-sdk to 0.25.1

* Use static lerobot.Scalars schema for Foxglove state topics

Replace the per-topic JSON schema derived from feature names with a single
static lerobot.Scalars schema: a scalars array of {label, value} objects. The
same schema fits any robot regardless of which observation/action features it
reports, and the label field lets Foxglove name each series automatically so
one filtered path plots every feature.

* add foxglove option to dataset viz

* Make Foxglove dataset playback loop the sole frame emitter

Address review: the listener no longer emits frames, it only mutates
playback state and queues a one-shot seek index that the playback loop
services. The loop is now the only caller of emit_frame, so concurrent
random access into the on-disk dataset / video decoder never overlaps.

Also remove the dead server_holder and tighten the _foxglove_safe_name
docstring to state what it does and why.

* Label Foxglove dataset scalars with feature dimension names

Use the dataset's per-dimension feature names (e.g. joint names) as the
Foxglove series labels for /observation/state and /action/state instead
of bare indices. LeRobot stores `names` inconsistently (flat list,
{category: [...]}, or {name: index}), so _feature_dim_names handles each
and falls back to indices on any unknown format or length mismatch.

* Make Foxglove server host bindable and refactor topic/channel handling

Pass display_ip through as the Foxglove WebSocket bind host (127.0.0.1
for local only, 0.0.0.0 for all interfaces) instead of always binding
locally. In lerobot-dataset-viz, fold the separate --port into --web-port
so one flag covers both the Rerun web viewer and the Foxglove server port.

Add a _foxglove_topic() helper and thread a per-topic channel cache
through the log helpers so dataset playback stays self-contained instead
of mutating the module-global cache. Promote SUCCESS to constants.py.

* feat(viz): add support for foxglove in rollout + add to viz tag

* fix(docs): remove misleading installation note

* fix(visualization): no duplicated prefix, consolidated norm + warnings log

* chore(viz): minor improvements

* refactor(viz): split files + autoplay + updated docs + added minimal tests

* fix(viz): right tags + warning

* feat(deprecated ws-port): removing rerun's depreacted ws-port parameter in dataset visualization

* chore(web ports): adding global variables for default foxglove/rerun web ports

* feat(depth): adding depth support to foxglove visualizer. Because of foxglove limitations (min and max values on RawImage cannot be set from the SDK), depth is normalized between [0,1] when a depth range is provided.

* fix(rerun depth range): making rerun depth range computation safe against missing stats

* chore(foxglove depth): make it simple, and make it work.

* fix(scaling): fixing depth frames scaling

---------

Co-authored-by: Roman Shtylman <roman@foxglove.dev>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>

docs/source/il_robots.mdx

@@ -126,7 +126,7 @@ import time
 from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
 from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun
+from lerobot.utils.visualization_utils import init_visualization, log_visualization_data, shutdown_visualization
 
 robot_config = SO101FollowerConfig(
     port="/dev/tty.usbmodem5AB90687491",
@@ -142,7 +142,7 @@ teleop_config = SO101LeaderConfig(
     id="my_leader_arm",
 )
 
-init_rerun(session_name="teleoperation")
+init_visualization("rerun", session_name="teleoperation")  # pass "foxglove" to stream to Foxglove instead
 
 robot = SO101Follower(robot_config)
 teleop_device = SO101Leader(teleop_config)
@@ -158,7 +158,7 @@ while True:
     observation = robot.get_observation()
     action = teleop_device.get_action()
     robot.send_action(action)
-    log_rerun_data(observation=observation, action=action)
+    log_visualization_data("rerun", observation=observation, action=action)
 
     elapsed_time = time.perf_counter() - start_time
     sleep_time = TIME_PER_FRAME - elapsed_time
@@ -223,7 +223,7 @@ from lerobot.teleoperators.so_leader.config_so_leader import SO101LeaderConfig
 from lerobot.teleoperators.so_leader.so_leader import SO101Leader
 from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_visualization
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.processor import make_default_processors
 
@@ -270,7 +270,7 @@ def main():
 
     # Initialize the keyboard listener and rerun visualization
     _, events = init_keyboard_listener()
-    init_rerun(session_name="recording")
+    init_visualization("rerun", session_name="recording")
 
     # Connect the robot and teleoperator
     robot.connect()

docs/source/using_dataset_tools.mdx

@@ -265,6 +265,8 @@ lerobot-dataset-viz \
 
 Once executed, the tool opens `rerun.io` and displays the camera streams, robot states, and actions for the selected episode.
 
+To use [Foxglove](https://foxglove.dev) instead of Rerun, install the extra add `--display-mode foxglove`. This starts a WebSocket server (connect the Foxglove app to `ws://127.0.0.1:8765`) that serves the episode as a seekable timeline you can play/pause and scrub.
+
 For advanced usage—including visualizing datasets stored on a remote server—run:
 
 ```bash

docs/source/video_encoding_parameters.mdx

@@ -6,12 +6,11 @@ Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel
 
 You can set these parameters from the CLI with `--dataset.rgb_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
 
-<Tip>
-  Video storage must be on for `rgb_encoder` to have any effect —
-  `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
-  recording default). With video off, inputs stay as images and `rgb_encoder` is
-  ignored.
-</Tip>
+> [!TIP]
+> Video storage must be on for `rgb_encoder` to have any effect —
+> `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
+> recording default). With video off, inputs stay as images and `rgb_encoder` is
+> ignored.
 
 For details on **when** frames are written vs. encoded (streaming vs. post-episode), queues, and other top-level `--dataset.*` switches, see [Streaming Video Encoding](./streaming_video_encoding). For an encoding-parameter comparison and experiments, see the [video-benchmark Space](https://huggingface.co/spaces/lerobot/video-benchmark).
 
@@ -43,12 +42,10 @@ lerobot-record \
 
 ## Tuning parameters
 
-<Tip warning={true}>
-The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
-
-Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.
-
-</Tip>
+> [!WARNING]
+> The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
+>
+> Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.
 
 All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
 
@@ -69,25 +66,92 @@ All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
 
 Depth maps (Intel RealSense, Reachy 2) are stored as their **own video streams** alongside the RGB streams. Raw depth (`uint16` millimetres or `float32` metres) can't survive an 8-bit codec, so LeRobot **quantizes** each map to a 12-bit code (`[0, 4095]`) — logarithmically by default, to match the `1/depth` error profile of depth sensors — then packs it into a high-bit-depth pixel format (`gray12le`) and encodes it with a 12-bit codec.
 
-```mermaid
-flowchart LR
-    A["Raw depth (uint16 mm / float32 m)"] --> B["Clip to depth_min, depth_max"]
-    B --> C["Quantize to 12-bit code 0–4095 (log or linear)"]
-    C --> D["Pack into gray12le"]
-    D --> E["Encode video (hevc Main 12)"]
-    E --> F[("MP4 + metadata: depth_min/max, shift, use_log")]
-    F -. "load time (depth_output_unit)" .-> G["Dequantize to mm or m"]
-
-    classDef input fill:#e3f2fd,stroke:#1565c0,color:#0d47a1;
-    classDef encode fill:#ede7f6,stroke:#5e35b1,color:#311b92;
-    classDef store fill:#fff8e1,stroke:#f9a825,color:#e65100;
-    classDef load fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20;
-
-    class A input;
-    class B,C,D,E encode;
-    class F store;
-    class G load;
-```
+<div style="margin:28px 0;padding:14px 0;">
+  <div style="margin:0 auto;display:flex;flex-wrap:wrap;justify-content:center;align-items:stretch;gap:6px;font-family:'Source Sans 3',ui-sans-serif,system-ui,sans-serif;font-size:14px;font-weight:600;color:#1B1B1D;">
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#DBEAFE;color:#1D4ED8;border-radius:9px;padding:8px 12px;">
+      <span>Raw depth</span>
+      <span style="font-size:11px;font-weight:400;color:#3B6FD4;white-space:nowrap;">
+        uint16 mm
+        <br />
+        float32 m
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <div style="border:2px dashed #C4B5FD;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:stretch;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#7E22CE;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Record time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Clip</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          to [depth_min,
+          <br />
+          depth_max]
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Quantize</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          12-bit codes 0–4095
+          <br />
+          log (default) or linear
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Pack</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          into gray12le
+          <br />
+          plane
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Encode</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          HEVC
+          <br />
+          Main 12
+        </span>
+      </span>
+    </div>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#FEF3C7;color:#B45309;border-radius:9px;padding:8px 12px;">
+      <span>MP4</span>
+      <span style="font-size:11px;font-weight:400;color:#C77D18;white-space:nowrap;">
+        stored
+        <br />
+        stream
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#34A06B;">
+      →
+    </span>
+    <div style="border:2px dashed #6EE7B7;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:center;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#047857;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Load time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#D1FAE5;color:#047857;border-radius:9px;padding:8px 12px;">
+        <span>Dequantize</span>
+        <span style="font-size:11px;font-weight:400;color:#059669;white-space:nowrap;">
+          to mm / m
+        </span>
+      </span>
+    </div>
+  </div>
+</div>
 
 Configure the depth pipeline through a parallel **`depth_encoder`** block (`DepthEncoderConfig`). It shares every `RGBEncoderConfig` field (`vcodec`, `pix_fmt`, `crf`, …) and adds four quantizer knobs, set via `--dataset.depth_encoder.<field>`:
 
@@ -134,9 +198,6 @@ lerobot-train \
 > [!TIP]
 > This is purely a decode-time presentation choice — it does **not** alter the stored video or its metadata, so the same dataset can be read as `mm` or `m` without re-encoding. It has no effect on datasets without depth cameras.
 
-> [!IMPORTANT]
-> Depth statistics in `meta/stats.json` are always computed in **millimetres**, regardless of the raw frame dtype.
-
 ---
 
 ## Persistence in dataset metadata
@@ -171,15 +232,81 @@ After the first episode of a video stream is encoded, the encoder configuration
 
 Two sources contribute to the `info` block:
 
-- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, plus `audio.*` if an audio stream is present.
-- **Encoder-derived** (taken from `RGBEncoderConfig` or `DepthEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
+<div style="display:flex;flex-wrap:wrap;gap:14px;margin:20px 0;font-family:'Source Sans 3',ui-sans-serif,system-ui,sans-serif;">
+  <div style="flex:1 1 280px;border:1px solid #BFDBFE;border-radius:12px;overflow:hidden;">
+    <div style="background:#DBEAFE;color:#1D4ED8;font-weight:700;font-size:14px;padding:8px 14px;">
+      Stream-derived
+    </div>
+    <div style="padding:12px 14px;">
+      <div style="font-size:13px;color:#4B5563;margin-bottom:10px;">
+        Read back from the encoded MP4 with PyAV.
+      </div>
+      <div style="display:flex;flex-wrap:wrap;gap:6px;">
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.height
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.width
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.codec
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.pix_fmt
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.fps
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.channels
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          is_depth_map
+        </code>
+        <code style="background:#EFF6FF;color:#1D4ED8;border-radius:6px;padding:2px 8px;font-size:12px;">
+          audio.*
+        </code>
+      </div>
+    </div>
+  </div>
+  <div style="flex:1 1 280px;border:1px solid #DDD6FE;border-radius:12px;overflow:hidden;">
+    <div style="background:#F3E8FF;color:#7E22CE;font-weight:700;font-size:14px;padding:8px 14px;">
+      Encoder-derived
+    </div>
+    <div style="padding:12px 14px;">
+      <div style="font-size:13px;color:#4B5563;margin-bottom:10px;">
+        Taken from <code style="font-size:12px;">RGBEncoderConfig</code> /{" "}
+        <code style="font-size:12px;">DepthEncoderConfig</code>.
+      </div>
+      <div style="display:flex;flex-wrap:wrap;gap:6px;">
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.g
+        </code>
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.crf
+        </code>
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.preset
+        </code>
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.fast_decode
+        </code>
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.video_backend
+        </code>
+        <code style="background:#FAF5FF;color:#7E22CE;border-radius:6px;padding:2px 8px;font-size:12px;">
+          video.extra_options
+        </code>
+      </div>
+    </div>
+  </div>
+</div>
 
-<Tip>
-  This block is populated **once**, from the **first** episode. It assumes every
-  episode in the dataset was encoded with the same `rgb_encoder`. Changing
-  encoder settings partway through a recording is not supported — the
-  `info.json` will only reflect the parameters used for the first episode.
-</Tip>
+> [!IMPORTANT]
+> This block is populated **once**, from the **first** episode. It assumes every
+> episode in the dataset was encoded with the same `rgb_encoder`. Changing
+> encoder settings partway through a recording is not supported — the
+> `info.json` will only reflect the parameters used for the first episode.
 
 ---
 
@@ -187,5 +314,35 @@ Two sources contribute to the `info` block:
 
 When aggregating datasets with `merge_datasets`, video files are concatenated as-is (no re-encoding), and encoder fields in `info.json` are merged per-key:
 
-- **Stream-derived fields must match** across sources: `video.codec`, `video.pix_fmt`, `video.height`, `video.width`, `video.fps`. Otherwise FFmpeg's concat demuxer fails.
-- **Encoder-tuning fields are merged loosely**: `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.extra_options`. If every source agrees, the value is kept; if not, it's set to `null` (or `{}` for `video.extra_options`) and a warning is logged.
+<div style="display:flex;flex-direction:column;gap:12px;margin:20px 0;font-family:'Source Sans 3',ui-sans-serif,system-ui,sans-serif;">
+  <div style="display:flex;gap:12px;align-items:flex-start;border-left:3px solid #F87171;background:#FEF2F2;border-radius:0 10px 10px 0;padding:12px 14px;">
+    <span style="flex:none;background:#FEE2E2;color:#B91C1C;font-weight:700;font-size:11px;text-transform:uppercase;letter-spacing:0.4px;border-radius:6px;padding:3px 8px;margin-top:1px;white-space:nowrap;">
+      Must match
+    </span>
+    <span style="font-size:14px;color:#1B1B1D;">
+      Stream-derived fields — <code style="font-size:12px;">video.codec</code>,{" "}
+      <code style="font-size:12px;">video.pix_fmt</code>,{" "}
+      <code style="font-size:12px;">video.height</code>,{" "}
+      <code style="font-size:12px;">video.width</code>,{" "}
+      <code style="font-size:12px;">video.fps</code> — must match across
+      sources, otherwise FFmpeg's concat demuxer fails.
+    </span>
+  </div>
+  <div style="display:flex;gap:12px;align-items:flex-start;border-left:3px solid #34D399;background:#ECFDF5;border-radius:0 10px 10px 0;padding:12px 14px;">
+    <span style="flex:none;background:#D1FAE5;color:#047857;font-weight:700;font-size:11px;text-transform:uppercase;letter-spacing:0.4px;border-radius:6px;padding:3px 8px;margin-top:1px;white-space:nowrap;">
+      Merged loosely
+    </span>
+    <span style="font-size:14px;color:#1B1B1D;">
+      Encoder-tuning fields — <code style="font-size:12px;">video.g</code>,{" "}
+      <code style="font-size:12px;">video.crf</code>,{" "}
+      <code style="font-size:12px;">video.preset</code>,{" "}
+      <code style="font-size:12px;">video.fast_decode</code>,{" "}
+      <code style="font-size:12px;">video.extra_options</code>. If every source
+      agrees, the value is kept; if not, it's set to{" "}
+      <code style="font-size:12px;">null</code> (or{" "}
+      <code style="font-size:12px;">{}</code> for{" "}
+      <code style="font-size:12px;">video.extra_options</code>) and a warning is
+      logged.
+    </span>
+  </div>
+</div>

pyproject.toml

@@ -125,6 +125,7 @@ hardware = [
 ]
 viz = [
     "rerun-sdk>=0.24.0,<0.34.0",
+    "foxglove-sdk>=0.25.1,<0.26.0",
 ]
 # ── User-facing composite extras (map to CLI scripts) ─────
 # lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.

src/lerobot/datasets/compute_stats.py

@@ -22,7 +22,6 @@ import numpy as np
 from lerobot.processor import RelativeActionsProcessorStep
 from lerobot.utils.constants import ACTION, OBS_STATE
 
-from .depth_utils import MM_PER_METRE
 from .io_utils import load_image_as_numpy
 
 DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99]
@@ -509,8 +508,8 @@ def compute_episode_stats(
     Note:
         For 'image'/'video' features, stats are computed per channel and kept with a
         leading channel axis (e.g. shape (3, 1, 1) for RGB). RGB stats are divided by
-        255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) are
-        instead canonicalized to millimetres regardless of the raw frame unit.
+        255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) skip
+        this rescaling and remain in their stored units.
     """
     if quantile_list is None:
         quantile_list = DEFAULT_QUANTILES
@@ -534,14 +533,9 @@ def compute_episode_stats(
         )
 
         if features[key]["dtype"] in ["image", "video"]:
-            if (features[key].get("info") or {}).get("is_depth_map", False):
-                # Depth stats are canonically stored in millimetres; metre (float) depth is
-                # scaled up, integer (millimetre) depth is left as-is.
-                normalization_factor = (
-                    1.0 / MM_PER_METRE if np.issubdtype(ep_ft_array.dtype, np.floating) else 1.0
-                )
-            else:
-                normalization_factor = 255.0
+            normalization_factor = (
+                255.0 if not (features[key].get("info") or {}).get("is_depth_map", False) else 1.0
+            )
             ep_stats[key] = {
                 k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0)
                 for k, v in ep_stats[key].items()

src/lerobot/datasets/depth_utils.py

@@ -39,7 +39,7 @@ from lerobot.configs.video import (
 from .image_writer import squeeze_single_channel
 from .pyav_utils import write_u16_plane
 
-MM_PER_METRE = 1000.0
+_MM_PER_METRE = 1000.0
 _UINT16_MAX = 65535
 
 
@@ -126,12 +126,12 @@ def quantize_depth(
 
     # Convert depth_min, depth_max, and shift to the resolved input unit.
     depth_min_u = (
-        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * MM_PER_METRE)
+        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * _MM_PER_METRE)
     )
     depth_max_u = (
-        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * MM_PER_METRE)
+        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * _MM_PER_METRE)
     )
-    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * _MM_PER_METRE)
 
     # Normalization and quantization is performed in the resolved input unit.
     if use_log:
@@ -236,7 +236,7 @@ def dequantize_depth(
 
         # mm path: round + clamp in float32, skipping the uint16 round-trip
         # when returning a tensor (torch.uint16 is poorly supported).
-        buf.mul_(MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
+        buf.mul_(_MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
         if output_tensor:
             return buf
         return buf.cpu().numpy().astype(np.uint16, copy=False)
@@ -259,7 +259,7 @@ def dequantize_depth(
     if output_unit == DEPTH_METER_UNIT:
         return torch.from_numpy(buf) if output_tensor else buf
 
-    np.multiply(buf, MM_PER_METRE, out=buf)
+    np.multiply(buf, _MM_PER_METRE, out=buf)
     np.rint(buf, out=buf)
     np.clip(buf, 0.0, _UINT16_MAX, out=buf)
     if output_tensor:

src/lerobot/datasets/video_utils.py

@@ -47,7 +47,7 @@ from lerobot.configs import (
 )
 from lerobot.utils.import_utils import get_safe_default_video_backend
 
-from .depth_utils import MM_PER_METRE, quantize_depth
+from .depth_utils import quantize_depth
 from .pyav_utils import get_pix_fmt_channels
 
 logger = logging.getLogger(__name__)
@@ -848,9 +848,6 @@ class _CameraEncoderThread(threading.Thread):
                 # Reshape CHW to (H*W, C) for per-channel stats
                 channels = img_downsampled.shape[0]
                 img_for_stats = img_downsampled.transpose(1, 2, 0).reshape(-1, channels)
-                # Depth stats are canonically stored in millimetres; metre (float) depth is scaled up.
-                if self.is_depth and np.issubdtype(frame_data.dtype, np.floating):
-                    img_for_stats = img_for_stats * MM_PER_METRE
                 stats_tracker.update(img_for_stats)
 
                 frame_count += 1

src/lerobot/rollout/configs.py

@@ -226,11 +226,14 @@ class RolloutConfig:
     device: str | None = None
     task: str = ""
     display_data: bool = False
-    # Display data on a remote Rerun server
+    # Visualization backend used when display_data is True: "rerun" or "foxglove".
+    display_mode: str = "rerun"
+    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
+    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
     display_ip: str | None = None
-    # Port of the remote Rerun server
+    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
     display_port: int | None = None
-    # Whether to display compressed images in Rerun
+    # Whether to display compressed (JPEG) images instead of raw frames
     display_compressed_images: bool = False
     # Use vocal synthesis to read events
     play_sounds: bool = True

src/lerobot/rollout/strategies/core.py

@@ -26,7 +26,7 @@ from lerobot.utils.action_interpolator import ActionInterpolator
 from lerobot.utils.constants import OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.robot_utils import precise_sleep
-from lerobot.utils.visualization_utils import log_rerun_data
+from lerobot.utils.visualization_utils import log_visualization_data
 
 from ..inference import InferenceEngine
 
@@ -162,11 +162,12 @@ class RolloutStrategy(abc.ABC):
         action_dict: dict | None,
         runtime_ctx: RuntimeContext,
     ) -> None:
-        """Log observation/action telemetry to Rerun if display_data is enabled."""
+        """Log observation/action telemetry to the visualization backend if display_data is enabled."""
         cfg = runtime_ctx.cfg
         if not cfg.display_data:
             return
-        log_rerun_data(
+        log_visualization_data(
+            cfg.display_mode,
             observation=obs_processed,
             action=action_dict,
             compress_images=cfg.display_compressed_images,

src/lerobot/rollout/strategies/episodic.py

@@ -44,7 +44,7 @@ from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import log_rerun_data
+from lerobot.utils.visualization_utils import log_visualization_data
 
 from ..configs import EpisodicStrategyConfig
 from ..context import RolloutContext
@@ -171,6 +171,7 @@ class EpisodicStrategy(RolloutStrategy):
                             fps=fps,
                             control_time_s=reset_time_s,
                             display_data=cfg.display_data,
+                            display_mode=cfg.display_mode,
                             display_compressed=display_compressed,
                         )
 
@@ -259,6 +260,7 @@ class EpisodicStrategy(RolloutStrategy):
         fps: float,
         control_time_s: float,
         display_data: bool,
+        display_mode: str,
         display_compressed: bool,
     ) -> None:
         """Reset-phase loop: teleop drives the robot if available, no recording."""
@@ -288,7 +290,8 @@ class EpisodicStrategy(RolloutStrategy):
 
                 if display_data:
                     obs_processed = processors.robot_observation_processor(obs)
-                    log_rerun_data(
+                    log_visualization_data(
+                        display_mode,
                         observation=obs_processed,
                         action=act_teleop,
                         compress_images=display_compressed,

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -59,6 +59,18 @@ distant$ lerobot-dataset-viz \
 local$ rerun rerun+http://IP:GRPC_PORT/proxy
 ```
 
+- Visualize data in Foxglove with a seekable, scrubbable timeline:
+```
+local$ lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --episode-index 0 \
+    --display-mode foxglove
+
+# then open the Foxglove app and connect to ws://127.0.0.1:8765
+```
+This starts a Foxglove WebSocket server that serves the episode on demand from the on-disk dataset,
+so you can play/pause and scrub anywhere in the episode using Foxglove's playback controls.
+
 """
 
 import argparse
@@ -73,9 +85,12 @@ import torch.utils.data
 import tqdm
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
+from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
 from lerobot.utils.utils import init_logging
 
+DEFAULT_FOXGLOVE_PORT = 8765
+DEFAULT_RERUN_PORT = 9090
+
 
 def get_feature_names(dataset: LeRobotDataset, key: str) -> list[str]:
     """Return per-dimension names for a feature from the dataset metadata.
@@ -108,6 +123,12 @@ def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     return hwc_uint8_numpy
 
 
+def to_hwc_float32_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
+    check_chw_float32(chw_float32_torch)
+    hwc_float32_numpy = chw_float32_torch.permute(1, 2, 0).numpy()
+    return hwc_float32_numpy
+
+
 def build_blueprint_from_dataset(dataset: LeRobotDataset):
     """Build a Rerun blueprint laying out camera images and time series for the given dataset.
 
@@ -126,32 +147,43 @@ def build_blueprint_from_dataset(dataset: LeRobotDataset):
             names = get_feature_names(dataset, key)
             styling = rr.SeriesLines(names=names)
             views.append(rrb.TimeSeriesView(origin=origin, name=origin, overrides={origin: styling}))
-    for key in (DONE, REWARD, "next.success"):
+    for key in (DONE, REWARD, SUCCESS):
         if key in dataset.features:
             views.append(rrb.TimeSeriesView(origin=key, name=key))
 
     return rrb.Blueprint(rrb.Grid(*views))
 
 
-def to_hwc_uint16_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
-    check_chw_float32(chw_float32_torch)
-    hwc_uint16_numpy = chw_float32_torch.round().type(torch.uint16).permute(1, 2, 0).numpy()
-    return hwc_uint16_numpy
-
-
 def visualize_dataset(
     dataset: LeRobotDataset,
     episode_index: int,
     batch_size: int = 32,
     num_workers: int = 0,
     mode: str = "local",
-    web_port: int = 9090,
+    web_port: int | None = None,
     grpc_port: int = 9876,
     save: bool = False,
     output_dir: Path | None = None,
     display_compressed_images: bool = False,
+    display_mode: str = "rerun",
+    host: str = "127.0.0.1",
+    autoplay: bool = True,
     **kwargs,
 ) -> Path | None:
+    if display_mode == "foxglove":
+        from lerobot.utils.foxglove_visualization import serve_foxglove_dataset_playback
+
+        logging.info("Starting Foxglove server")
+        serve_foxglove_dataset_playback(
+            dataset,
+            episode_index,
+            host=host,
+            port=web_port if web_port is not None else DEFAULT_FOXGLOVE_PORT,
+            compress_images=display_compressed_images,
+            autoplay=autoplay,
+        )
+        return None
+
     if save:
         assert output_dir is not None, (
             "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
@@ -188,14 +220,20 @@ def visualize_dataset(
     if mode == "distant":
         server_uri = rr.serve_grpc(grpc_port=grpc_port)
         logging.info(f"Connect to a Rerun Server: rerun rerun+http://IP:{grpc_port}/proxy")
-        rr.serve_web_viewer(open_browser=False, web_port=web_port, connect_to=server_uri)
+        rr.serve_web_viewer(
+            open_browser=False,
+            web_port=web_port if web_port is not None else DEFAULT_RERUN_PORT,
+            connect_to=server_uri,
+        )
 
     logging.info("Logging to Rerun")
 
     # Use the dataset's q01/q99 depth statistics for robust depth range bounds
     depth_ranges = {}
     for key in dataset.meta.depth_keys:
-        stats = dataset.meta.stats[key]
+        stats = (dataset.meta.stats or {}).get(key)
+        if not stats:
+            continue
         lo = stats["q01"] if "q01" in stats else stats["min"]
         hi = stats["q99"] if "q99" in stats else stats["max"]
         depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
@@ -213,11 +251,11 @@ def visualize_dataset(
             # display each camera image (or depth map)
             for key in dataset.meta.camera_keys:
                 if key in dataset.meta.depth_keys:
-                    depth = to_hwc_uint16_numpy(batch[key][i])
+                    depth = to_hwc_float32_numpy(batch[key][i])
                     depth_entity = rr.DepthImage(
                         depth,
                         colormap=rr.components.Colormap.Viridis,
-                        depth_range=depth_ranges[key],
+                        depth_range=depth_ranges.get(key),
                     )
                     rr.log(key, entity=depth_entity)
                 else:
@@ -239,8 +277,8 @@ def visualize_dataset(
             if REWARD in batch:
                 rr.log(REWARD, rr.Scalars(batch[REWARD][i].item()))
 
-            if "next.success" in batch:
-                rr.log("next.success", rr.Scalars(batch["next.success"][i].item()))
+            if SUCCESS in batch:
+                rr.log(SUCCESS, rr.Scalars(batch[SUCCESS][i].item()))
 
     # save .rrd locally
     if mode == "local" and save:
@@ -312,13 +350,11 @@ def main():
     parser.add_argument(
         "--web-port",
         type=int,
-        default=9090,
-        help="Web port for rerun.io when `--mode distant` is set.",
-    )
-    parser.add_argument(
-        "--ws-port",
-        type=int,
-        help="deprecated, please use --grpc-port instead.",
+        default=None,
+        help=(
+            "Web/WebSocket port. For rerun `--mode distant` it is the web viewer port (default 9090); "
+            "for `--display-mode foxglove` it is the server bind port (default 8765)."
+        ),
     )
     parser.add_argument(
         "--grpc-port",
@@ -351,24 +387,56 @@ def main():
     parser.add_argument(
         "--display-compressed-images",
         action="store_true",
-        help="If set, display compressed images in Rerun instead of uncompressed ones.",
+        help="If set, display compressed (JPEG) images instead of uncompressed ones.",
+    )
+
+    parser.add_argument(
+        "--display-mode",
+        type=str,
+        default="rerun",
+        choices=["rerun", "foxglove"],
+        help=(
+            "Visualization backend. 'rerun' uses the Rerun viewer (--mode/--save/--*-port apply). "
+            "'foxglove' starts a Foxglove WebSocket server that serves the episode as a seekable, "
+            "scrubbable timeline; connect the Foxglove app to ws://HOST:PORT (--host/--web-port)."
+        ),
+    )
+    parser.add_argument(
+        "--host",
+        type=str,
+        default="127.0.0.1",
+        help=(
+            "Host to bind the Foxglove WebSocket server to when `--display-mode foxglove` is set "
+            "(127.0.0.1 for local only, 0.0.0.0 for all interfaces)."
+        ),
+    )
+    parser.add_argument(
+        "--no-autoplay",
+        dest="autoplay",
+        action="store_false",
+        help=(
+            "For `--display-mode foxglove`: don't start playing automatically when a client "
+            "connects; wait for play to be pressed in the Foxglove app instead."
+        ),
     )
 
     args = parser.parse_args()
+
+    if args.display_mode == "foxglove":
+        rerun_only = ("mode", "save", "output_dir", "grpc_port", "batch_size", "num_workers")
+        ignored = [name for name in rerun_only if getattr(args, name) != parser.get_default(name)]
+        if ignored:
+            logging.warning(
+                "These flags only apply to `--display-mode rerun` and are ignored with "
+                "`--display-mode foxglove`: %s.",
+                ", ".join(f"--{name.replace('_', '-')}" for name in ignored),
+            )
+
     kwargs = vars(args)
     repo_id = kwargs.pop("repo_id")
     root = kwargs.pop("root")
     tolerance_s = kwargs.pop("tolerance_s")
 
-    if kwargs["ws_port"] is not None:
-        logging.warning(
-            "--ws-port is deprecated and will be removed in future versions. Please use --grpc-port instead."
-        )
-        logging.warning("Setting grpc_port to ws_port value.")
-        kwargs["grpc_port"] = kwargs.pop("ws_port")
-    else:
-        kwargs.pop("ws_port")  # Always remove ws_port from kwargs
-
     init_logging()
     logging.info("Loading dataset")
     dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], root=root, tolerance_s=tolerance_s)

src/lerobot/scripts/lerobot_record.py

@@ -38,6 +38,9 @@ lerobot-record \\
     --display_data=true
 ```
 
+To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove`` (then connect the
+Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``).
+
 Example recording with bimanual so100:
 ```shell
 lerobot-record \\
@@ -157,7 +160,11 @@ from lerobot.utils.utils import (
     init_logging,
     log_say,
 )
-from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+from lerobot.utils.visualization_utils import (
+    init_visualization,
+    log_visualization_data,
+    shutdown_visualization,
+)
 
 
 @dataclass
@@ -168,11 +175,14 @@ class RecordConfig:
     teleop: TeleoperatorConfig | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Display data on a remote Rerun server
+    # Visualization backend used when display_data is True: "rerun" or "foxglove".
+    display_mode: str = "rerun"
+    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
+    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
     display_ip: str | None = None
-    # Port of the remote Rerun server
+    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
     display_port: int | None = None
-    # Whether to  display compressed images in Rerun
+    # Whether to display compressed (JPEG) images instead of raw frames
     display_compressed_images: bool = False
     # Use vocal synthesis to read events.
     play_sounds: bool = True
@@ -233,6 +243,7 @@ def record_loop(
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
+    display_mode: str = "rerun",
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -327,8 +338,11 @@ def record_loop(
             dataset.add_frame(frame)
 
         if display_data:
-            log_rerun_data(
-                observation=obs_processed, action=action_values, compress_images=display_compressed_images
+            log_visualization_data(
+                display_mode,
+                observation=obs_processed,
+                action=action_values,
+                compress_images=display_compressed_images,
             )
 
         dt_s = time.perf_counter() - start_loop_t
@@ -354,7 +368,9 @@ def record(
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_rerun(session_name="recording", ip=cfg.display_ip, port=cfg.display_port)
+        init_visualization(
+            cfg.display_mode, session_name="recording", ip=cfg.display_ip, port=cfg.display_port
+        )
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -464,6 +480,7 @@ def record(
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
+                    display_mode=cfg.display_mode,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -485,6 +502,7 @@ def record(
                         control_time_s=cfg.dataset.reset_time_s,
                         single_task=cfg.dataset.single_task,
                         display_data=cfg.display_data,
+                        display_mode=cfg.display_mode,
                     )
 
                 if events["rerecord_episode"]:
@@ -510,6 +528,9 @@ def record(
         if listener is not None:
             listener.stop()
 
+        if cfg.display_data:
+            shutdown_visualization(cfg.display_mode)
+
         if cfg.dataset.push_to_hub:
             if dataset and dataset.num_episodes > 0:
                 dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)

src/lerobot/scripts/lerobot_rollout.py

@@ -145,6 +145,9 @@ Usage examples
         --dataset.rgb_encoder.vcodec=h264 \\
         --dataset.rgb_encoder.preset=fast \\
         --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2}
+
+    # Stream to Foxglove instead of Rerun:
+    # add --display_mode=foxglove, then connect the Foxglove app to ws://127.0.0.1:8765.
 """
 
 import logging
@@ -190,7 +193,7 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_visualization, shutdown_visualization
 
 logger = logging.getLogger(__name__)
 
@@ -201,8 +204,13 @@ def rollout(cfg: RolloutConfig):
     init_logging()
 
     if cfg.display_data:
-        logger.info("Initializing Rerun visualization (ip=%s, port=%s)", cfg.display_ip, cfg.display_port)
-        init_rerun(session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
+        logger.info(
+            "Initializing %s visualization (ip=%s, port=%s)",
+            cfg.display_mode,
+            cfg.display_ip,
+            cfg.display_port,
+        )
+        init_visualization(cfg.display_mode, session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
 
     signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
     shutdown_event = signal_handler.shutdown_event
@@ -227,6 +235,8 @@ def rollout(cfg: RolloutConfig):
         logger.info("Interrupted by user")
     finally:
         strategy.teardown(ctx)
+        if cfg.display_data:
+            shutdown_visualization(cfg.display_mode)
 
     logger.info("Rollout finished")
 

src/lerobot/scripts/lerobot_teleoperate.py

@@ -31,6 +31,22 @@ lerobot-teleoperate \
     --display_data=true
 ```
 
+To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove``
+(then connect the Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``):
+
+```shell
+lerobot-teleoperate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --robot.id=black \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=blue \
+    --display_data=true \
+    --display_mode=foxglove
+```
+
 Example teleoperation with bimanual so100:
 
 ```shell
@@ -108,7 +124,11 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging, move_cursor_up
-from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun
+from lerobot.utils.visualization_utils import (
+    init_visualization,
+    log_visualization_data,
+    shutdown_visualization,
+)
 
 
 @dataclass
@@ -121,11 +141,14 @@ class TeleoperateConfig:
     teleop_time_s: float | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Display data on a remote Rerun server
+    # Visualization backend used when display_data is True: "rerun" or "foxglove".
+    display_mode: str = "rerun"
+    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
+    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
     display_ip: str | None = None
-    # Port of the remote Rerun server
+    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
     display_port: int | None = None
-    # Whether to  display compressed images in Rerun
+    # Whether to display compressed (JPEG) images instead of raw frames
     display_compressed_images: bool = False
 
 
@@ -137,6 +160,7 @@ def teleop_loop(
     robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction],
     robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation],
     display_data: bool = False,
+    display_mode: str = "rerun",
     duration: float | None = None,
     display_compressed_images: bool = False,
 ):
@@ -149,8 +173,10 @@ def teleop_loop(
         teleop: The teleoperator device instance providing control actions.
         robot: The robot instance being controlled.
         fps: The target frequency for the control loop in frames per second.
-        display_data: If True, fetches robot observations and displays them in the console and Rerun.
-        display_compressed_images: If True, compresses images before sending them to Rerun for display.
+        display_data: If True, fetches robot observations and displays them in the console and the
+            visualization backend.
+        display_mode: Visualization backend to use when display_data is True ("rerun" or "foxglove").
+        display_compressed_images: If True, compresses images before sending them to the backend for display.
         duration: The maximum duration of the teleoperation loop in seconds. If None, the loop runs indefinitely.
         teleop_action_processor: An optional pipeline to process raw actions from the teleoperator.
         robot_action_processor: An optional pipeline to process actions before they are sent to the robot.
@@ -187,7 +213,8 @@ def teleop_loop(
             # Process robot observation through pipeline
             obs_transition = robot_observation_processor(obs)
 
-            log_rerun_data(
+            log_visualization_data(
+                display_mode,
                 observation=obs_transition,
                 action=teleop_action,
                 compress_images=display_compressed_images,
@@ -215,7 +242,9 @@ def teleoperate(cfg: TeleoperateConfig):
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_rerun(session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port)
+        init_visualization(
+            cfg.display_mode, session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port
+        )
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -235,6 +264,7 @@ def teleoperate(cfg: TeleoperateConfig):
             robot=robot,
             fps=cfg.fps,
             display_data=cfg.display_data,
+            display_mode=cfg.display_mode,
             duration=cfg.teleop_time_s,
             teleop_action_processor=teleop_action_processor,
             robot_action_processor=robot_action_processor,
@@ -245,7 +275,7 @@ def teleoperate(cfg: TeleoperateConfig):
         pass
     finally:
         if cfg.display_data:
-            shutdown_rerun()
+            shutdown_visualization(cfg.display_mode)
         teleop.disconnect()
         robot.disconnect()
 

src/lerobot/utils/constants.py

@@ -37,6 +37,7 @@ ACTION_TOKEN_MASK = ACTION + ".token_mask"
 REWARD = "next.reward"
 TRUNCATED = "next.truncated"
 DONE = "next.done"
+SUCCESS = "next.success"
 INFO = "info"
 
 ROBOTS = "robots"

src/lerobot/utils/foxglove_visualization.py

@@ -0,0 +1,651 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Foxglove visualization backend.
+
+Live control-loop streaming (:func:`log_foxglove_data`) and seekable dataset playback
+(:func:`serve_foxglove_dataset_playback`) over a Foxglove WebSocket server. Callers usually select a
+backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
+importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
+"""
+
+import logging
+import numbers
+import time
+
+import cv2
+import numpy as np
+
+from lerobot.types import RobotAction, RobotObservation
+
+from .constants import (
+    ACTION,
+    ACTION_PREFIX,
+    DONE,
+    OBS_IMAGES,
+    OBS_PREFIX,
+    OBS_STATE,
+    OBS_STR,
+    REWARD,
+    SUCCESS,
+    TRUNCATED,
+)
+from .import_utils import require_package
+
+# Static schema shared by all scalar topics. Each message carries a flat list of ``{label, value}``
+# pairs rather than one field per feature, so the same schema fits any robot regardless of which
+# observation/action features it reports. The ``label`` field name is what Foxglove looks for to name
+# each series automatically, so a single filtered path plots every feature, e.g.
+# ``/observation/state.scalars[:]``.
+_SCALARS_SCHEMA = {
+    "type": "object",
+    "title": "lerobot.Scalars",
+    "properties": {
+        "scalars": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "label": {"type": "string"},
+                    "value": {"type": "number"},
+                },
+            },
+        }
+    },
+}
+
+
+def _is_scalar(x):
+    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
+        isinstance(x, np.ndarray) and x.ndim == 0
+    )
+
+
+def init_foxglove(host: str = "127.0.0.1", port: int | None = 8765) -> None:
+    """
+    Starts a Foxglove WebSocket server for visualizing the control loop.
+
+    Connect to it from the Foxglove app at ``ws://<host>:<port>``. Calling this
+    more than once is a no-op while a server is already running.
+
+    Args:
+        host: Host interface to bind the WebSocket server to.
+        port: Port to bind the WebSocket server to (defaults to 8765).
+    """
+
+    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
+    import foxglove
+
+    # Live-stream state lives as attributes on ``log_foxglove_data``:
+    # ``.server`` is the shared WebSocket server and
+    # ``.channels`` caches one Foxglove channel per topic
+    if getattr(log_foxglove_data, "server", None) is not None:
+        return
+    log_foxglove_data.server = foxglove.start_server(host=host, port=port or 8765)
+    log_foxglove_data.channels = {}
+
+
+def shutdown_foxglove() -> None:
+    """Stops the Foxglove WebSocket server and clears cached channels."""
+
+    server = getattr(log_foxglove_data, "server", None)
+    if server is not None:
+        server.stop()
+    log_foxglove_data.server = None
+    log_foxglove_data.channels = {}
+
+
+def _foxglove_safe_name(name: str) -> str:
+    """Replace ``.`` with ``_`` so a feature name is a single Foxglove topic-path segment.
+
+    Foxglove treats ``.`` as a path separator, so an unsanitized name like ``observation.images.front``
+    would split into nested segments instead of naming one topic.
+    """
+
+    return name.replace(".", "_")
+
+
+def _foxglove_topic(key: str, *, is_image: bool = False) -> str:
+    """Build the Foxglove topic for a feature ``key``.
+
+    Camera features map to a per-source image topic (``/observation/images/<name>``); scalar features
+    share one aggregate topic per source: ``/observation/state`` for observations, ``/action/state``
+    for actions.
+    """
+
+    if is_image:
+        name = str(key)
+        for prefix in (f"{OBS_IMAGES}.", OBS_PREFIX):
+            if name.startswith(prefix):
+                name = name[len(prefix) :]
+                break
+        return f"/{OBS_STR}/images/{_foxglove_safe_name(name)}"
+    source = ACTION if (str(key).startswith(ACTION_PREFIX) or str(key) == ACTION) else OBS_STR
+    return f"/{source}/state"
+
+
+def _log_foxglove_scalars(
+    topic: str, values: dict[str, float], *, channels: dict | None = None, log_time: int | None = None
+) -> None:
+    """Log scalars on a typed JSON channel using the static :data:`_SCALARS_SCHEMA`.
+
+    ``values`` is an ordered mapping of feature name to value; it is emitted as a ``scalars`` array of
+    ``{label, value}`` objects. Insertion order is preserved so series stay stable across messages.
+
+    ``channels`` is the per-topic channel cache to reuse (defaults to the live-stream cache on
+    :func:`log_foxglove_data`; dataset playback passes its own local cache to stay self-contained).
+    ``log_time`` is the message time in nanoseconds; when ``None`` the server's receive time is used.
+    """
+
+    if not values:
+        return
+
+    import foxglove
+
+    if channels is None:
+        channels = log_foxglove_data.channels
+    channel = channels.get(topic)
+    if channel is None:
+        channel = channels[topic] = foxglove.Channel(topic, schema=_SCALARS_SCHEMA, message_encoding="json")
+    msg = {"scalars": [{"label": label, "value": value} for label, value in values.items()]}
+    if log_time is None:
+        channel.log(msg)
+    else:
+        channel.log(msg, log_time=log_time)
+
+
+def _labeled_scalars(name: str, values, labels: list[str] | None = None) -> dict[str, float]:
+    """Expand a 1D sequence into ``{label: value}`` entries with a consistent fallback."""
+
+    flat = [float(v) for v in values]
+    if labels is None or len(labels) != len(flat):
+        labels = [f"{name}_{i}" for i in range(len(flat))]
+    return dict(zip(labels, flat, strict=True))
+
+
+def _log_foxglove_image(
+    topic: str,
+    frame_id: str,
+    arr: np.ndarray,
+    *,
+    compress_images: bool,
+    channels: dict | None = None,
+    log_time: int | None = None,
+    depth_range: tuple[float, float] | None = None,
+    raw_depth_values: bool = False,
+) -> None:
+    """Log an image on a cached per-topic channel.
+
+    The encoding is chosen from the channel count and dtype: a single-channel ``float`` or ``uint16``
+    frame is a depth map (``32FC1``/``16UC1``), single-channel ``uint8`` is ``mono8``, 3 => ``rgb8``
+    (float input assumed in [0, 1], cast to uint8), 4 => ``rgba8``; other counts are skipped with a
+    warning. When ``compress_images`` is set, ``rgb8`` is JPEG-encoded instead.
+
+    Args:
+        topic: Foxglove topic to log on.
+        frame_id: Frame id stamped on the message.
+        arr: Image as HWC or CHW (CHW is transposed to HWC), any dtype.
+        compress_images: JPEG-encode ``rgb8`` frames; ignored for other encodings.
+        channels: Per-topic channel cache to reuse (see :func:`_log_foxglove_scalars`).
+        log_time: Message time in nanoseconds, also written to the header timestamp; when ``None``
+            the server's receive time is used.
+        depth_range: ``(lo, hi)`` clip bounds in a depth frame's own input units. Depth frames
+            (``32FC1``/``16UC1``) are rescaled onto Foxglove's default display max for their encoding
+            (``1.0`` / ``10000``) so they show with sensible contrast; ``depth_range`` sets the source
+            range, else the frame's own min/max is used. Ignored for ``mono8``/``rgb8``/``rgba8``.
+        raw_depth_values: If True, depth values are not rescaled and are logged as is.
+    """
+
+    from foxglove.channels import CompressedImageChannel, RawImageChannel
+    from foxglove.messages import CompressedImage, RawImage, Timestamp
+
+    if channels is None:
+        channels = log_foxglove_data.channels
+    time_ns = time.time_ns() if log_time is None else log_time
+    timestamp = Timestamp(sec=time_ns // 1_000_000_000, nsec=time_ns % 1_000_000_000)
+    log_kwargs = {} if log_time is None else {"log_time": log_time}
+
+    # Convert CHW -> HWC when needed (mirrors log_rerun_data).
+    if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
+        arr = np.transpose(arr, (1, 2, 0))
+    height, width = arr.shape[0], arr.shape[1]
+    n_channels = 1 if arr.ndim == 2 else arr.shape[2]
+
+    if n_channels == 1 and arr.dtype != np.uint8:
+        # Depth map: infer the encoding from the dtype.
+        encoding, target_dtype, value_max = (
+            ("32FC1", np.float32, 1.0)
+            if np.issubdtype(arr.dtype, np.floating)
+            else ("16UC1", np.uint16, 10000.0)
+        )
+        if not raw_depth_values:
+            # Rescale onto the encoding's display max with respect to the given depth_range.
+            lo, hi = depth_range if depth_range is not None else (float(arr.min()), float(arr.max()))
+            arr = arr.clip(lo, hi).astype(np.float32)
+            arr = (arr - lo) / ((hi - lo) if hi > lo else 1.0) * value_max
+        arr = np.ascontiguousarray(arr, dtype=target_dtype)
+    else:
+        if n_channels == 3 and np.issubdtype(arr.dtype, np.floating):
+            arr = (arr * 255.0).clip(0, 255)
+        arr = np.ascontiguousarray(arr, dtype=np.uint8)
+
+        if compress_images and n_channels == 3:
+            buf_src = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
+            _, buf = cv2.imencode(".jpg", buf_src)
+            channel = channels.get(topic)
+            if channel is None:
+                channel = channels[topic] = CompressedImageChannel(topic=topic)
+            channel.log(
+                CompressedImage(timestamp=timestamp, frame_id=frame_id, data=buf.tobytes(), format="jpeg"),
+                **log_kwargs,
+            )
+            return
+
+        encoding = {1: "mono8", 3: "rgb8", 4: "rgba8"}.get(n_channels)
+        if encoding is None:
+            logging.warning(
+                "Foxglove: skipping image on topic '%s' with unsupported shape %s (%d channels); "
+                "expected 1 (mono8/16UC1/32FC1), 3 (rgb8), or 4 (rgba8) channels.",
+                topic,
+                tuple(arr.shape),
+                n_channels,
+            )
+            return
+
+    channel = channels.get(topic)
+    if channel is None:
+        channel = channels[topic] = RawImageChannel(topic=topic)
+    channel.log(
+        RawImage(
+            timestamp=timestamp,
+            frame_id=frame_id,
+            width=width,
+            height=height,
+            encoding=encoding,
+            step=width * n_channels * arr.itemsize,
+            data=arr.tobytes(),
+        ),
+        **log_kwargs,
+    )
+
+
+def log_foxglove_data(
+    observation: RobotObservation | None = None,
+    action: RobotAction | None = None,
+    compress_images: bool = False,
+) -> None:
+    """
+    Logs observation and action data to a Foxglove WebSocket server for real-time visualization.
+
+    Mirrors ``log_rerun_data`` but emits Foxglove messages over the server started by
+    :func:`init_foxglove`. Data is mapped as follows:
+    - Scalars (and elements of 1D arrays) are accumulated per source and logged on the
+      ``/observation/state`` and ``/action/state`` topics as typed JSON messages using the static
+      ``lerobot.Scalars`` schema: a ``scalars`` array of ``{label, value}`` objects (see
+      :data:`_SCALARS_SCHEMA`). The ``label`` field lets Foxglove name each series automatically, so
+      ``/observation/state.scalars[:].value`` plots every feature at once.
+    - 3D NumPy arrays that resemble images are transposed from CHW to HWC when needed and logged on a
+      per-source topic (e.g. ``/observation/images/front``) as a ``RawImage`` (or a JPEG
+      ``CompressedImage`` when ``compress_images`` is True).
+
+    Args:
+        observation: An optional dictionary containing observation data to log.
+        action: An optional dictionary containing action data to log.
+        compress_images: Whether to JPEG-compress images before logging to save bandwidth in exchange
+            for CPU and quality.
+    """
+
+    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
+
+    if getattr(log_foxglove_data, "server", None) is None:
+        raise RuntimeError("init_foxglove() must be called before log_foxglove_data().")
+
+    now = time.time_ns()
+
+    if observation:
+        obs_scalars: dict[str, float] = {}
+        for k, v in observation.items():
+            if v is None:
+                continue
+            key = k[len(OBS_PREFIX) :] if str(k).startswith(OBS_PREFIX) else str(k)
+            if _is_scalar(v):
+                obs_scalars[key] = float(v)
+            elif isinstance(v, np.ndarray):
+                if v.ndim == 1:
+                    obs_scalars.update(_labeled_scalars(key, v))
+                else:
+                    _log_foxglove_image(
+                        _foxglove_topic(k, is_image=True),
+                        key,
+                        v,
+                        compress_images=compress_images,
+                        log_time=now,
+                    )
+        _log_foxglove_scalars(_foxglove_topic(OBS_STATE), obs_scalars, log_time=now)
+
+    if action:
+        action_scalars: dict[str, float] = {}
+        for k, v in action.items():
+            if v is None:
+                continue
+            key = k[len(ACTION_PREFIX) :] if str(k).startswith(ACTION_PREFIX) else str(k)
+            if _is_scalar(v):
+                action_scalars[key] = float(v)
+            elif isinstance(v, np.ndarray):
+                action_scalars.update(_labeled_scalars(key, v.flatten()))
+        _log_foxglove_scalars(_foxglove_topic(ACTION), action_scalars, log_time=now)
+
+
+# ── Dataset playback over a Foxglove WebSocket server ─────────────────────
+# A LeRobotDataset is random-access on disk, so rather than fire-and-forget a forward stream we
+# advertise a seekable timeline and serve frames on demand for whatever time the user scrubs/plays
+# to in the Foxglove app. This relies on the SDK's PlaybackControl capability.
+
+
+def _feature_dim_names(feature: dict | None) -> list[str] | None:
+    """Best-effort per-dimension series labels for a 1D feature, or ``None`` to fall back to indices.
+
+    LeRobot records a feature's ``names`` inconsistently: a flat list (``["x", "y"]``), a category
+    mapping (``{"motors": ["motor_0", "motor_1"]}``), or a name->index mapping
+    (``{"delta_x": 0, "delta_y": 1}``). Each is handled, but labels are only returned when their count
+    matches the feature's 1D shape, so a malformed/mismatched ``names`` can't silently mislabel series.
+    """
+
+    if not feature:
+        return None
+    shape = feature.get("shape")
+    dim = shape[0] if shape and len(shape) == 1 else None
+    names = feature.get("names")
+    labels: list[str] | None = None
+    if isinstance(names, dict):
+        values = list(names.values())
+        if values and all(isinstance(v, (list, tuple)) for v in values):
+            labels = [str(n) for group in values for n in group]
+        elif values and all(isinstance(v, int) and not isinstance(v, bool) for v in values):
+            labels = [name for name, _ in sorted(names.items(), key=lambda kv: kv[1])]
+    elif isinstance(names, (list, tuple)):
+        labels = [str(n) for n in names]
+    if labels is not None and dim is not None and len(labels) == dim:
+        return labels
+    return None
+
+
+def _frame_to_scalars(sample: dict, key: str, labels: list[str] | None = None) -> dict[str, float]:
+    """Flatten a frame's vector/scalar feature ``key`` into ``{label: value}`` entries.
+
+    ``labels`` provides one name per dimension (from the dataset's feature metadata); when absent or
+    the wrong length, dimensions fall back to ``{name}_{i}`` (the short feature name), matching the
+    live stream so series names agree. A scalar feature becomes a single entry. Missing or ``None``
+    features yield an empty mapping.
+    """
+
+    v = sample.get(key)
+    if v is None:
+        return {}
+    arr = v.numpy() if hasattr(v, "numpy") else np.asarray(v)
+    if key.startswith(OBS_PREFIX):
+        name = key[len(OBS_PREFIX) :]
+    elif key.startswith(ACTION_PREFIX):
+        name = key[len(ACTION_PREFIX) :]
+    else:
+        name = key
+    if arr.ndim == 0:
+        return {name: float(arr)}
+    return _labeled_scalars(name, arr.flatten(), labels)
+
+
+def serve_foxglove_dataset_playback(
+    dataset,
+    episode_index: int,
+    *,
+    host: str = "127.0.0.1",
+    port: int = 8765,
+    compress_images: bool = False,
+    autoplay: bool = True,
+) -> None:
+    """Serve a single dataset episode to Foxglove as a seekable, scrubbable timeline.
+
+    Starts a Foxglove WebSocket server advertising the ``PlaybackControl`` capability over the
+    episode's time range. The Foxglove app drives play/pause/seek/speed; a background thread and a
+    ``ServerListener`` read frames from the on-disk ``dataset`` on demand and log them stamped at
+    their dataset timestamps, so the user can scrub anywhere in the episode. Blocks until interrupted.
+
+    Args:
+        dataset: A ``LeRobotDataset`` loaded for the single episode to visualize.
+        episode_index: Index of the episode being visualized (used only for the session name).
+        host: Host interface to bind the WebSocket server to.
+        port: Port to bind the WebSocket server to.
+        compress_images: Whether to JPEG-compress camera frames before logging.
+        autoplay: If True, start playing automatically as soon as a client connects, instead of
+            waiting for the user to press play in the Foxglove app.
+    """
+
+    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
+    import bisect
+    import threading
+
+    import foxglove
+    from foxglove.websocket import (
+        Capability,
+        PlaybackCommand,
+        PlaybackControlRequest,
+        PlaybackState,
+        PlaybackStatus,
+        ServerListener,
+    )
+
+    # Per-frame timestamps in nanoseconds (read straight from the table, no video decode).
+    times_ns = [int(round(float(t) * 1e9)) for t in dataset.hf_dataset["timestamp"]]
+    n_frames = len(times_ns)
+    if n_frames == 0:
+        raise ValueError("Cannot visualize an empty episode.")
+    first_ns, last_ns = times_ns[0], times_ns[-1]
+    camera_keys = list(dataset.meta.camera_keys)
+    # Dataset-wide q01/q99 depth bounds (fallback min/max) used to normalize depth to [0, 1].
+    depth_ranges: dict[str, tuple[float, float]] = {}
+    for key in dataset.meta.depth_keys:
+        stats = (dataset.meta.stats or {}).get(key)
+        if not stats:
+            continue
+        lo = stats["q01"] if "q01" in stats else stats["min"]
+        hi = stats["q99"] if "q99" in stats else stats["max"]
+        depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
+    # Per-dimension series labels from the dataset metadata (e.g. joint names), computed once.
+    scalar_labels = {
+        OBS_STATE: _feature_dim_names(dataset.meta.features.get(OBS_STATE)),
+        ACTION: _feature_dim_names(dataset.meta.features.get(ACTION)),
+    }
+    # Local channel cache so the playback server is self-contained and doesn't touch the live-stream cache.
+    channels: dict = {}
+
+    def emit_frame(i: int) -> None:
+        """Log every channel for frame ``i`` stamped at its dataset timestamp."""
+        sample = dataset[i]
+        log_time = times_ns[i]
+        for key in camera_keys:
+            arr = sample.get(key)
+            if arr is None:
+                continue
+            arr = arr.numpy() if hasattr(arr, "numpy") else np.asarray(arr)
+            _log_foxglove_image(
+                _foxglove_topic(key, is_image=True),
+                key,
+                arr,
+                compress_images=compress_images,
+                channels=channels,
+                log_time=log_time,
+                depth_range=depth_ranges.get(key),
+                raw_depth_values=True,
+            )
+        _log_foxglove_scalars(
+            _foxglove_topic(OBS_STATE),
+            _frame_to_scalars(sample, OBS_STATE, scalar_labels[OBS_STATE]),
+            channels=channels,
+            log_time=log_time,
+        )
+        _log_foxglove_scalars(
+            _foxglove_topic(ACTION),
+            _frame_to_scalars(sample, ACTION, scalar_labels[ACTION]),
+            channels=channels,
+            log_time=log_time,
+        )
+        episode_scalars = {}
+        for feat, label in (
+            (DONE, "done"),
+            (TRUNCATED, "truncated"),
+            (REWARD, "reward"),
+            (SUCCESS, "success"),
+        ):
+            v = sample.get(feat)
+            if v is not None:
+                episode_scalars[label] = float(v)
+        _log_foxglove_scalars("/episode/state", episode_scalars, channels=channels, log_time=log_time)
+
+    lock = threading.Lock()
+    stop_event = threading.Event()
+    # Shared playback state, guarded by ``lock``. ``seek_idx`` is a one-shot request set by the
+    # listener and serviced by the playback loop, which is the *only* thread that emits frames (so
+    # concurrent random access into the on-disk dataset / video decoder never overlaps).
+    state = {
+        "status": PlaybackStatus.Paused,
+        "cursor": first_ns,
+        "speed": 1.0,
+        "last_idx": -1,
+        "seek_idx": None,
+    }
+
+    def index_at(t_ns: int) -> int:
+        return max(0, min(n_frames - 1, bisect.bisect_right(times_ns, t_ns) - 1))
+
+    # One-shot latch so autoplay fires only on the first client subscription.
+    autoplay_started = threading.Event()
+
+    class _PlaybackListener(ServerListener):
+        def on_subscribe(self, client, channel):
+            # Start playing automatically once a client actually connects (subscribes). Using the
+            # subscribe hook, rather than starting in Playing up front, means the timeline doesn't
+            # advance before anyone is watching. Fires once; the user can still pause/seek after.
+            if not autoplay:
+                return
+            with lock:
+                if autoplay_started.is_set() or state["status"] != PlaybackStatus.Paused:
+                    return
+                autoplay_started.set()
+                state["status"] = PlaybackStatus.Playing
+                cursor, speed = state["cursor"], state["speed"]
+            server.broadcast_playback_state(PlaybackState(PlaybackStatus.Playing, cursor, speed, False, ""))
+
+        def on_playback_control_request(self, req: PlaybackControlRequest):
+            # Only mutate state here; the playback loop performs all frame emission.
+            with lock:
+                did_seek = False
+                if req.seek_time is not None:
+                    cursor = max(first_ns, min(last_ns, req.seek_time))
+                    state["cursor"] = cursor
+                    state["last_idx"] = state["seek_idx"] = index_at(cursor)
+                    did_seek = True
+                if req.playback_speed and req.playback_speed > 0:
+                    state["speed"] = req.playback_speed
+                if req.playback_command == PlaybackCommand.Play:
+                    # Restarting from the end replays from the beginning.
+                    if state["cursor"] >= last_ns:
+                        state["cursor"] = first_ns
+                        state["last_idx"] = state["seek_idx"] = 0
+                        did_seek = True
+                    state["status"] = PlaybackStatus.Playing
+                elif req.playback_command == PlaybackCommand.Pause:
+                    state["status"] = PlaybackStatus.Paused
+                status, cursor, speed = state["status"], state["cursor"], state["speed"]
+                request_id = req.request_id or ""
+            return PlaybackState(status, cursor, speed, did_seek, request_id)
+
+    server = foxglove.start_server(
+        name=f"{dataset.repo_id}/episode_{episode_index}",
+        host=host,
+        port=port,
+        capabilities=[Capability.PlaybackControl, Capability.Time],
+        server_listener=_PlaybackListener(),
+        playback_time_range=(first_ns, last_ns),
+    )
+
+    def playback_loop() -> None:
+        # Cap how far the cursor may advance in a single tick. A slow frame decode (or any stall)
+        # would otherwise make ``dt`` huge and produce one enormous catch-up batch; clamping it makes
+        # playback trail wall-clock under a slow decoder while each tick emits a bounded frame range.
+        max_tick_dt_s = 0.25
+        prev = time.monotonic()
+        while not stop_event.is_set():
+            time.sleep(1.0 / 60.0)
+            ended = False
+            speed = 1.0
+            with lock:
+                now = time.monotonic()
+                dt = min(now - prev, max_tick_dt_s)
+                prev = now
+                # A queued seek is always serviced, even while paused, so scrubbing updates the view.
+                work = []
+                seek_idx = state["seek_idx"]
+                if seek_idx is not None:
+                    state["seek_idx"] = None
+                    work.append(seek_idx)
+                if state["status"] == PlaybackStatus.Playing:
+                    cursor = state["cursor"] + int(dt * 1e9 * state["speed"])
+                    start_idx = state["last_idx"] + 1
+                    if cursor >= last_ns:
+                        cursor, target, ended = last_ns, n_frames - 1, True
+                    else:
+                        target = index_at(cursor)
+                    state["cursor"] = cursor
+                    work.extend(range(start_idx, target + 1))
+                    # cursor only grows while playing (seeks reset last_idx in the listener), so
+                    # target >= last_idx here; a plain assignment is correct and clearer than max().
+                    state["last_idx"] = target
+                    if ended:
+                        state["status"] = PlaybackStatus.Ended
+                if not work:
+                    continue
+                cursor, speed = state["cursor"], state["speed"]
+            # Emit outside the lock; this is the only thread that calls emit_frame. Re-check
+            # stop_event between frames so shutdown stays responsive even mid-batch.
+            for i in work:
+                if stop_event.is_set():
+                    break
+                emit_frame(i)
+            server.broadcast_time(cursor)
+            if ended:
+                server.broadcast_playback_state(PlaybackState(PlaybackStatus.Ended, cursor, speed, False, ""))
+
+    # Emit the first frame so channels are advertised (done before the loop starts, so emission stays
+    # single-threaded). Late-connecting clients re-receive frames once they seek/play.
+    emit_frame(0)
+    with lock:
+        state["last_idx"] = 0
+    server.broadcast_time(first_ns)
+    server.broadcast_playback_state(PlaybackState(PlaybackStatus.Paused, first_ns, 1.0, True, ""))
+
+    thread = threading.Thread(target=playback_loop, name="foxglove-playback", daemon=True)
+    thread.start()
+
+    print(f"Foxglove server running. Connect the Foxglove app to ws://{host}:{port}")
+    print("Use the playback controls in Foxglove to play/pause and scrub the episode. Ctrl-C to exit.")
+    try:
+        while not stop_event.is_set():
+            time.sleep(0.5)
+    except KeyboardInterrupt:
+        print("Ctrl-C received. Exiting.")
+    finally:
+        stop_event.set()
+        thread.join(timeout=2.0)
+        server.stop()
+        channels.clear()

src/lerobot/utils/rerun_visualization.py

@@ -0,0 +1,184 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Rerun visualization backend.
+
+Live control-loop streaming to the Rerun viewer (:func:`log_rerun_data`). Callers usually select a
+backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
+importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
+"""
+
+import numbers
+import os
+
+import numpy as np
+
+from lerobot.types import RobotAction, RobotObservation
+
+from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
+from .import_utils import require_package
+
+
+def _is_scalar(x):
+    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
+        isinstance(x, np.ndarray) and x.ndim == 0
+    )
+
+
+def init_rerun(
+    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
+) -> None:
+    """
+    Initializes the Rerun SDK for visualizing the control loop.
+
+    Args:
+        session_name: Name of the Rerun session.
+        ip: Optional IP for connecting to a Rerun server.
+        port: Optional port for connecting to a Rerun server.
+    """
+
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    log_rerun_data.blueprint = None  # Reset blueprint cache for new session
+
+    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
+    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
+    rr.init(session_name)
+    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
+    if ip and port:
+        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
+    else:
+        rr.spawn(memory_limit=memory_limit)
+
+
+def shutdown_rerun() -> None:
+    """Shuts down the Rerun SDK gracefully."""
+
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    rr.rerun_shutdown()
+
+
+def _build_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]):
+    """Build a Rerun blueprint laying out camera images, observation and action scalars in separate views.
+
+    Camera images, observation and action scalars are arranged in a grid.
+    """
+
+    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
+    import rerun.blueprint as rrb
+
+    views = [rrb.Spatial2DView(origin=path, name=path) for path in sorted(image_paths)]
+
+    if observation_paths:
+        views.append(rrb.TimeSeriesView(name="observation", contents=sorted(observation_paths)))
+    if action_paths:
+        views.append(rrb.TimeSeriesView(name="action", contents=sorted(action_paths)))
+
+    return rrb.Blueprint(rrb.Grid(*views))
+
+
+def _ensure_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]) -> None:
+    """Build and send the blueprint once, from the first observation and action data."""
+    if getattr(log_rerun_data, "blueprint", None) is not None:
+        return
+
+    if not (observation_paths or action_paths or image_paths):
+        return
+
+    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
+    import rerun as rr
+
+    blueprint = _build_blueprint(observation_paths, action_paths, image_paths)
+    log_rerun_data.blueprint = blueprint
+    rr.send_blueprint(blueprint)
+
+
+def log_rerun_data(
+    observation: RobotObservation | None = None,
+    action: RobotAction | None = None,
+    compress_images: bool = False,
+) -> None:
+    """
+    Logs observation and action data to Rerun for real-time visualization.
+
+    This function iterates through the provided observation and action dictionaries and sends their contents
+    to the Rerun viewer. It handles different data types appropriately:
+    - Scalars values (floats, ints) are logged as `rr.Scalars`.
+    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
+      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
+    - 1D NumPy arrays are logged as a single `rr.Scalars` batch under one entity path, so that every
+      dimension shares the same view instead of being split across one view per element.
+    - Multi-dimensional **action** arrays are flattened and logged as a single `rr.Scalars` batch.
+
+    Keys are automatically namespaced with "observation." or "action." if not already present.
+
+    On the first call, a blueprint is built and sent so observation and action scalars get separate
+    time-series views and each image gets its own spatial view.
+
+    Args:
+        observation: An optional dictionary containing observation data to log.
+        action: An optional dictionary containing action data to log.
+        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
+    """
+
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    observation_paths: set[str] = set()
+    action_paths: set[str] = set()
+    image_paths: set[str] = set()
+
+    if observation:
+        for k, v in observation.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+                observation_paths.add(key)
+            elif isinstance(v, np.ndarray):
+                arr = v
+                # Convert CHW -> HWC when needed
+                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
+                    arr = np.transpose(arr, (1, 2, 0))
+                if arr.ndim == 1:
+                    rr.log(key, rr.Scalars(arr.astype(float)))
+                    observation_paths.add(key)
+                else:
+                    if arr.shape[-1] == 1:
+                        img_entity = rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
+                    else:
+                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
+                    rr.log(key, entity=img_entity, static=True)
+                    image_paths.add(key)
+
+    if action:
+        for k, v in action.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+                action_paths.add(key)
+            elif isinstance(v, np.ndarray):
+                # Flatten any (incl. higher-dimensional) array into a single batched Scalars
+                rr.log(key, rr.Scalars(v.reshape(-1).astype(float)))
+                action_paths.add(key)
+
+    _ensure_blueprint(observation_paths, action_paths, image_paths)

src/lerobot/utils/visualization_utils.py

@@ -12,166 +12,68 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numbers
-import os
+"""Backend-agnostic visualization dispatch.
 
-import numpy as np
+Selects a visualization backend at runtime via a display-mode string (e.g. a ``--display_mode`` CLI
+flag) so callers never branch on the backend. The concrete implementations live in
+:mod:`lerobot.utils.rerun_visualization` and :mod:`lerobot.utils.foxglove_visualization`; importing
+this module does not import ``rerun`` or ``foxglove`` (each backend imports its SDK lazily behind a
+``require_package`` guard).
+"""
 
 from lerobot.types import RobotAction, RobotObservation
 
-from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
-from .import_utils import require_package
+from .foxglove_visualization import init_foxglove, log_foxglove_data, shutdown_foxglove
+from .rerun_visualization import init_rerun, log_rerun_data, shutdown_rerun
+
+# Visualization backends selectable at runtime via a display-mode string (e.g. a --display_mode flag).
+VISUALIZATION_MODES = ("rerun", "foxglove")
 
 
-def init_rerun(
-    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
+def init_visualization(
+    display_mode: str,
+    *,
+    session_name: str = "lerobot_control_loop",
+    ip: str | None = None,
+    port: int | None = None,
 ) -> None:
-    """
-    Initializes the Rerun SDK for visualizing the control loop.
+    """Initializes the visualization backend selected by ``display_mode``.
 
-    Args:
-        session_name: Name of the Rerun session.
-        ip: Optional IP for connecting to a Rerun server.
-        port: Optional port for connecting to a Rerun server.
+    For ``"rerun"``, ``ip``/``port`` point at an optional remote Rerun server. For ``"foxglove"``,
+    ``ip`` is the interface to bind the WebSocket server to (``127.0.0.1`` for local only, ``0.0.0.0``
+    for all interfaces) and ``port`` is its port.
     """
 
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    log_rerun_data.blueprint = None  # Reset blueprint cache for new session
-
-    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
-    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
-    rr.init(session_name)
-    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
-    if ip and port:
-        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
+    if display_mode == "rerun":
+        init_rerun(session_name=session_name, ip=ip, port=port)
+    elif display_mode == "foxglove":
+        init_foxglove(host=ip or "127.0.0.1", port=port)
     else:
-        rr.spawn(memory_limit=memory_limit)
+        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
 
 
-def shutdown_rerun() -> None:
-    """Shuts down the Rerun SDK gracefully."""
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    rr.rerun_shutdown()
-
-
-def _is_scalar(x):
-    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
-        isinstance(x, np.ndarray) and x.ndim == 0
-    )
-
-
-def _build_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]):
-    """Build a Rerun blueprint laying out camera images, observation and action scalars in separate views.
-
-    Camera images, observation and action scalars are arranged in a grid.
-    """
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun.blueprint as rrb
-
-    views = [rrb.Spatial2DView(origin=path, name=path) for path in sorted(image_paths)]
-
-    if observation_paths:
-        views.append(rrb.TimeSeriesView(name="observation", contents=sorted(observation_paths)))
-    if action_paths:
-        views.append(rrb.TimeSeriesView(name="action", contents=sorted(action_paths)))
-
-    return rrb.Blueprint(rrb.Grid(*views))
-
-
-def _ensure_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]) -> None:
-    """Build and send the blueprint once, from the first observation and action data."""
-    if getattr(log_rerun_data, "blueprint", None) is not None:
-        return
-
-    if not (observation_paths or action_paths or image_paths):
-        return
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun as rr
-
-    blueprint = _build_blueprint(observation_paths, action_paths, image_paths)
-    log_rerun_data.blueprint = blueprint
-    rr.send_blueprint(blueprint)
-
-
-def log_rerun_data(
+def log_visualization_data(
+    display_mode: str,
     observation: RobotObservation | None = None,
     action: RobotAction | None = None,
     compress_images: bool = False,
 ) -> None:
-    """
-    Logs observation and action data to Rerun for real-time visualization.
+    """Logs observation/action data to the backend selected by ``display_mode``."""
 
-    This function iterates through the provided observation and action dictionaries and sends their contents
-    to the Rerun viewer. It handles different data types appropriately:
-    - Scalars values (floats, ints) are logged as `rr.Scalars`.
-    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
-      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
-    - 1D NumPy arrays are logged as a single `rr.Scalars` batch under one entity path, so that every
-      dimension shares the same view instead of being split across one view per element.
-    - Multi-dimensional **action** arrays are flattened and logged as a single `rr.Scalars` batch.
+    if display_mode == "rerun":
+        log_rerun_data(observation=observation, action=action, compress_images=compress_images)
+    elif display_mode == "foxglove":
+        log_foxglove_data(observation=observation, action=action, compress_images=compress_images)
+    else:
+        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
 
-    Keys are automatically namespaced with "observation." or "action." if not already present.
 
-    On the first call, a blueprint is built and sent so observation and action scalars get separate
-    time-series views and each image gets its own spatial view.
+def shutdown_visualization(display_mode: str) -> None:
+    """Shuts down the backend selected by ``display_mode``."""
 
-    Args:
-        observation: An optional dictionary containing observation data to log.
-        action: An optional dictionary containing action data to log.
-        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
-    """
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    observation_paths: set[str] = set()
-    action_paths: set[str] = set()
-    image_paths: set[str] = set()
-
-    if observation:
-        for k, v in observation.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                observation_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                arr = v
-                # Convert CHW -> HWC when needed
-                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
-                    arr = np.transpose(arr, (1, 2, 0))
-                if arr.ndim == 1:
-                    rr.log(key, rr.Scalars(arr.astype(float)))
-                    observation_paths.add(key)
-                else:
-                    if arr.shape[-1] == 1:
-                        img_entity = rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
-                    else:
-                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
-                    rr.log(key, entity=img_entity, static=True)
-                    image_paths.add(key)
-
-    if action:
-        for k, v in action.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                action_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                # Flatten any (incl. higher-dimensional) array into a single batched Scalars
-                rr.log(key, rr.Scalars(v.reshape(-1).astype(float)))
-                action_paths.add(key)
-
-    _ensure_blueprint(observation_paths, action_paths, image_paths)
+    if display_mode == "rerun":
+        shutdown_rerun()
+    elif display_mode == "foxglove":
+        shutdown_foxglove()
+    else:
+        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")

tests/datasets/test_depth.py

@@ -245,44 +245,3 @@ class TestFeatureFileRouting:
 
         dataset.save_episode()
         dataset.finalize()
-
-
-# ── 5. Depth stats unit canonicalization (millimetres) ────────────────
-
-
-class TestDepthStatsUnit:
-    """Depth stats are always stored in millimetres, regardless of raw frame dtype."""
-
-    NUM_FRAMES = 4
-
-    @pytest.mark.parametrize("use_videos", [False, True])
-    def test_stats_canonicalized_to_mm(self, tmp_path, features_factory, use_videos):
-        """Float (metre) and integer (millimetre) depth over the same physical range
-        yield identical millimetre-scale stats."""
-        from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-        def _record(depth_dtype, root):
-            features = features_factory(
-                camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH, use_videos=use_videos
-            )
-            dataset = LeRobotDataset.create(
-                repo_id=DUMMY_REPO_ID,
-                fps=DEFAULT_FPS,
-                features=features,
-                root=root,
-                use_videos=use_videos,
-                streaming_encoding=use_videos,
-            )
-            add_frames(dataset, num_frames=self.NUM_FRAMES, depth_dtype=depth_dtype)
-            dataset.save_episode()
-            dataset.finalize()
-            return np.asarray(dataset.meta.stats[DEPTH_KEY]["mean"]).reshape(-1)
-
-        # add_frames ramps float depth over 0.1–10 m and integer depth over 100–10000 mm
-        # (the same physical range), so canonicalized stats must match.
-        mean_m = _record(np.float32, tmp_path / "ds_m")
-        mean_mm = _record(np.uint16, tmp_path / "ds_mm")
-
-        # Float (metre) input is scaled to millimetres, not left in the single-digit metre range.
-        assert mean_m.item() > 50.0
-        np.testing.assert_allclose(mean_m, mean_mm, rtol=0.05)

tests/fixtures/dataset_factories.py

@@ -49,18 +49,16 @@ from tests.fixtures.constants import (
 )
 
 
-def add_frames(dataset: LeRobotDataset, num_frames: int, depth_dtype: np.dtype = np.uint16) -> None:
+def add_frames(dataset: LeRobotDataset, num_frames: int) -> None:
     """Append ``num_frames`` synthetic frames to ``dataset``.
 
-    Generates per-feature payloads from ``dataset.meta``: depth ramps (``depth_dtype``,
-    default ``uint16`` millimetres; pass ``np.float32`` for metres) for keys in
-    ``dataset.meta.depth_keys``, uint8 random noise for video/image keys, and float32
-    zeros for everything else. ``DEFAULT_FEATURES`` (timestamp, frame_index, ...) are
-    auto-populated by ``add_frame`` and skipped here.
+    Generates per-feature payloads from ``dataset.meta``: uint16 depth ramps for
+    keys in ``dataset.meta.depth_keys``, uint8 random noise for video/image keys,
+    and float32 zeros for everything else. ``DEFAULT_FEATURES`` (timestamp,
+    frame_index, ...) are auto-populated by ``add_frame`` and skipped here.
     """
     video_keys = dataset.meta.video_keys
     depth_keys = dataset.meta.depth_keys
-    depth_is_float = np.issubdtype(depth_dtype, np.floating)
     # Smooth gradient base reused per (H, W) to keep depth frames cheap to
     # encode (HEVC Main 12 hates white noise).
     _depth_base_cache: dict[tuple[int, int], np.ndarray] = {}
@@ -72,14 +70,11 @@ def add_frames(dataset: LeRobotDataset, num_frames: int, depth_dtype: np.dtype =
             shape = ft["shape"]
             if key in depth_keys:
                 h, w, _ = shape
-                # Float depth is expressed in metres, integer depth in millimetres.
-                lo, hi = (0.1, 10.0) if depth_is_float else (100.0, 10_000.0)
                 base = _depth_base_cache.setdefault(
                     (h, w),
-                    np.linspace(lo, hi, h * w, dtype=np.float32).reshape(h, w, 1),
+                    np.linspace(100.0, 10_000.0, h * w, dtype=np.float32).reshape(h, w, 1),
                 )
-                step = (0.05 if depth_is_float else 50.0) * i
-                frame[key] = (base + step).clip(0, 65535).astype(depth_dtype)
+                frame[key] = (base + 50.0 * i).clip(0, 65535).astype(np.uint16)
             elif key in video_keys:
                 frame[key] = np.random.randint(0, 256, shape, dtype=np.uint8)
             else:

tests/utils/test_foxglove_visualization.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for the Foxglove backend's pure helpers.
+
+These cover topic naming, series labelling and feature-name parsing. They import
+``foxglove_visualization`` directly and need NO ``foxglove`` extra: the SDK is imported lazily inside
+the functions that talk to the server, so the helpers below run in the base test tier.
+"""
+
+import numpy as np
+
+from lerobot.utils import foxglove_visualization as fv
+from lerobot.utils.constants import ACTION, OBS_STATE
+
+
+def test_foxglove_safe_name_collapses_dots():
+    assert fv._foxglove_safe_name("observation.images.front") == "observation_images_front"
+    assert fv._foxglove_safe_name("plain") == "plain"
+
+
+def test_foxglove_topic_image_strips_prefix_without_doubling_images():
+    # Fully-qualified camera key -> single clean segment (no doubled "images").
+    assert fv._foxglove_topic("observation.images.front", is_image=True) == "/observation/images/front"
+    # A nested camera name keeps its structure via safe-name collapsing.
+    assert (
+        fv._foxglove_topic("observation.images.wrist.left", is_image=True) == "/observation/images/wrist_left"
+    )
+    # Bare camera name (as real robots emit).
+    assert fv._foxglove_topic("front", is_image=True) == "/observation/images/front"
+
+
+def test_foxglove_topic_scalar_sources():
+    assert fv._foxglove_topic(OBS_STATE) == "/observation/state"
+    assert fv._foxglove_topic("observation.environment_state") == "/observation/state"
+    assert fv._foxglove_topic(ACTION) == "/action/state"
+    assert fv._foxglove_topic("action.delta") == "/action/state"
+
+
+def test_labeled_scalars_uses_labels_then_index_fallback():
+    assert fv._labeled_scalars("state", np.array([1.0, 2.0, 3.0])) == {
+        "state_0": 1.0,
+        "state_1": 2.0,
+        "state_2": 3.0,
+    }
+    assert fv._labeled_scalars("state", [1.0, 2.0], ["pan", "lift"]) == {"pan": 1.0, "lift": 2.0}
+    # Wrong-length labels fall back to index naming (never silently mislabels).
+    assert fv._labeled_scalars("q", [1.0, 2.0], ["only_one"]) == {"q_0": 1.0, "q_1": 2.0}
+
+
+def test_frame_to_scalars_matches_live_labeling_and_handles_scalar():
+    frame = {OBS_STATE: np.array([1.0, 2.0])}
+    # No metadata -> {short_name}_{i}, identical to the live-stream fallback.
+    assert fv._frame_to_scalars(frame, OBS_STATE) == fv._labeled_scalars("state", np.array([1.0, 2.0]))
+    assert fv._frame_to_scalars(frame, OBS_STATE) == {"state_0": 1.0, "state_1": 2.0}
+    # Metadata labels are honored.
+    assert fv._frame_to_scalars(frame, OBS_STATE, ["pan", "lift"]) == {"pan": 1.0, "lift": 2.0}
+    # A 0-d scalar becomes a single entry named by the short feature name.
+    assert fv._frame_to_scalars({ACTION: np.array(5.0)}, ACTION) == {"action": 5.0}
+    # A missing feature yields an empty mapping.
+    assert fv._frame_to_scalars({}, OBS_STATE) == {}
+
+
+def test_feature_dim_names_formats():
+    # Flat list of names.
+    assert fv._feature_dim_names({"shape": [2], "names": ["x", "y"]}) == ["x", "y"]
+    # Category mapping (dict of lists).
+    assert fv._feature_dim_names({"shape": [2], "names": {"motors": ["m0", "m1"]}}) == ["m0", "m1"]
+    # name -> index mapping (returned sorted by index).
+    assert fv._feature_dim_names({"shape": [2], "names": {"delta_x": 0, "delta_y": 1}}) == [
+        "delta_x",
+        "delta_y",
+    ]
+    # Bool values must NOT be treated as an index map (bool is a subclass of int).
+    assert fv._feature_dim_names({"shape": [2], "names": {"a": True, "b": False}}) is None
+    # Mismatched length -> None (won't silently mislabel).
+    assert fv._feature_dim_names({"shape": [3], "names": ["x", "y"]}) is None
+    # Missing / absent names -> None.
+    assert fv._feature_dim_names(None) is None
+    assert fv._feature_dim_names({"shape": [2]}) is None
+
+
+def test_is_scalar():
+    assert fv._is_scalar(1.0)
+    assert fv._is_scalar(np.float32(2.0))
+    assert fv._is_scalar(np.array(3.0))  # 0-d array
+    assert not fv._is_scalar(np.array([1.0, 2.0]))
+    assert not fv._is_scalar("x")

tests/utils/test_rerun_visualization.py

@@ -0,0 +1,310 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib
+import sys
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+pytest.importorskip("rerun", reason="rerun-sdk is required (install lerobot[viz])")
+
+from lerobot.types import TransitionKey
+from lerobot.utils.constants import OBS_STATE
+
+
+@pytest.fixture
+def mock_rerun(monkeypatch):
+    """
+    Provide a mock `rerun` module (and `rerun.blueprint` submodule) so tests don't
+    depend on the real library. Also reload the module-under-test so it binds to
+    this mock `rr`.
+    """
+    calls = []
+    blueprints = []
+
+    class DummyScalar:
+        def __init__(self, value):
+            # Scalars may be built from a single float or from a 1D array batch.
+            self.value = value
+
+    class DummyImage:
+        def __init__(self, arr):
+            self.arr = arr
+
+        def compress(self, *a, **k):
+            return self
+
+    class DummyDepthImage:
+        def __init__(self, arr, colormap=None):
+            self.arr = arr
+            self.colormap = colormap
+
+    def dummy_log(key, obj=None, **kwargs):
+        # Accept either positional `obj` or keyword `entity` and record remaining kwargs.
+        if obj is None and "entity" in kwargs:
+            obj = kwargs.pop("entity")
+        calls.append((key, obj, kwargs))
+
+    def dummy_send_blueprint(blueprint, *a, **k):
+        blueprints.append(blueprint)
+
+    # Mock the `rerun.blueprint` submodule used to build the layout.
+    dummy_rrb = SimpleNamespace(
+        Spatial2DView=lambda origin=None, name=None: SimpleNamespace(
+            kind="Spatial2DView", origin=origin, name=name
+        ),
+        TimeSeriesView=lambda name=None, contents=None: SimpleNamespace(
+            kind="TimeSeriesView", name=name, contents=contents
+        ),
+        Grid=lambda *views: SimpleNamespace(kind="Grid", views=list(views)),
+        Blueprint=lambda root: SimpleNamespace(kind="Blueprint", root=root),
+    )
+
+    dummy_rr = SimpleNamespace(
+        __name__="rerun",
+        __package__="rerun",
+        __spec__=SimpleNamespace(name="rerun", submodule_search_locations=None),
+        Scalars=DummyScalar,
+        Image=DummyImage,
+        DepthImage=DummyDepthImage,
+        components=SimpleNamespace(Colormap=SimpleNamespace(Viridis="viridis")),
+        log=dummy_log,
+        send_blueprint=dummy_send_blueprint,
+        init=lambda *a, **k: None,
+        spawn=lambda *a, **k: None,
+        blueprint=dummy_rrb,
+    )
+
+    # Inject fake modules into sys.modules (both `rerun` and `rerun.blueprint`).
+    monkeypatch.setitem(sys.modules, "rerun", dummy_rr)
+    monkeypatch.setitem(sys.modules, "rerun.blueprint", dummy_rrb)
+
+    # Now import and reload the module under test, to bind to our rerun mock
+    import lerobot.utils.rerun_visualization as rv
+
+    importlib.reload(rv)
+
+    # Expose the reloaded module, the call recorder and the captured blueprints
+    yield rv, calls, blueprints
+
+
+def _keys(calls):
+    """Helper to extract just the keys logged to rr.log"""
+    return [k for (k, _obj, _kw) in calls]
+
+
+def _obj_for(calls, key):
+    """Find the first object logged under a given key."""
+    for k, obj, _kw in calls:
+        if k == key:
+            return obj
+    raise KeyError(f"Key {key} not found in calls: {calls}")
+
+
+def _kwargs_for(calls, key):
+    for k, _obj, kw in calls:
+        if k == key:
+            return kw
+    raise KeyError(f"Key {key} not found in calls: {calls}")
+
+
+def _views_by_kind(blueprint, kind):
+    """Return the views of a given kind from the (single) blueprint's grid."""
+    return [v for v in blueprint.root.views if v.kind == kind]
+
+
+def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
+    rv, calls, blueprints = mock_rerun
+
+    # Build EnvTransition dict
+    obs = {
+        f"{OBS_STATE}.temperature": np.float32(25.0),
+        # CHW image should be converted to HWC for rr.Image
+        "observation.camera": np.zeros((3, 10, 20), dtype=np.uint8),
+    }
+    act = {
+        "action.throttle": 0.7,
+        # 1D array should be logged as a single Scalars batch under one entity path
+        "action.vector": np.array([1.0, 2.0], dtype=np.float32),
+    }
+    transition = {
+        TransitionKey.OBSERVATION: obs,
+        TransitionKey.ACTION: act,
+    }
+
+    # Extract observation and action data from transition like in the real call sites
+    obs_data = transition.get(TransitionKey.OBSERVATION, {})
+    action_data = transition.get(TransitionKey.ACTION, {})
+    rv.log_rerun_data(observation=obs_data, action=action_data)
+
+    # We expect:
+    # - observation.state.temperature -> Scalars
+    # - observation.camera -> Image (HWC) with static=True
+    # - action.throttle -> Scalars
+    # - action.vector -> single Scalars batch (no per-element suffix)
+    expected_keys = {
+        f"{OBS_STATE}.temperature",
+        "observation.camera",
+        "action.throttle",
+        "action.vector",
+    }
+    assert set(_keys(calls)) == expected_keys
+
+    # Check scalar types and values
+    temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature")
+    assert type(temp_obj).__name__ == "DummyScalar"
+    assert float(temp_obj.value) == pytest.approx(25.0)
+
+    throttle_obj = _obj_for(calls, "action.throttle")
+    assert type(throttle_obj).__name__ == "DummyScalar"
+    assert float(throttle_obj.value) == pytest.approx(0.7)
+
+    # 1D vector logged as a single batched Scalars under one entity path
+    vec = _obj_for(calls, "action.vector")
+    assert type(vec).__name__ == "DummyScalar"
+    np.testing.assert_allclose(np.asarray(vec.value), [1.0, 2.0])
+
+    # Check image handling: CHW -> HWC
+    img_obj = _obj_for(calls, "observation.camera")
+    assert type(img_obj).__name__ == "DummyImage"
+    assert img_obj.arr.shape == (10, 20, 3)  # transposed
+    assert _kwargs_for(calls, "observation.camera").get("static", False) is True  # static=True for images
+
+    # A blueprint should have been built and sent exactly once, and cached on the function.
+    assert len(blueprints) == 1
+    assert rv.log_rerun_data.blueprint is blueprints[0]
+
+    bp = blueprints[0]
+    # One spatial view per image path
+    spatial_views = _views_by_kind(bp, "Spatial2DView")
+    assert {v.origin for v in spatial_views} == {"observation.camera"}
+
+    # One time-series view each for observation and action scalars
+    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
+    assert set(ts_views) == {"observation", "action"}
+    assert ts_views["observation"].contents == [f"{OBS_STATE}.temperature"]
+    assert ts_views["action"].contents == ["action.throttle", "action.vector"]
+
+
+def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
+    rv, calls, blueprints = mock_rerun
+
+    # First dict without prefixes treated as observation
+    # Second dict without prefixes treated as action
+    obs_plain = {
+        "temp": 1.5,
+        # Already HWC image => should stay as-is
+        "img": np.zeros((5, 6, 3), dtype=np.uint8),
+        "none": None,  # should be skipped
+    }
+    act_plain = {
+        "throttle": 0.3,
+        "vec": np.array([9, 8, 7], dtype=np.float32),
+    }
+
+    # Extract observation and action data from list like the old function logic did
+    # First dict was treated as observation, second as action
+    rv.log_rerun_data(observation=obs_plain, action=act_plain)
+
+    # Expected keys with auto-prefixes. The 1D vector is a single batched Scalars.
+    expected = {
+        "observation.temp",
+        "observation.img",
+        "action.throttle",
+        "action.vec",
+    }
+    logged = set(_keys(calls))
+    assert logged == expected
+
+    # Scalars
+    t = _obj_for(calls, "observation.temp")
+    assert type(t).__name__ == "DummyScalar"
+    assert float(t.value) == pytest.approx(1.5)
+
+    throttle = _obj_for(calls, "action.throttle")
+    assert type(throttle).__name__ == "DummyScalar"
+    assert float(throttle.value) == pytest.approx(0.3)
+
+    # Image stays HWC
+    img = _obj_for(calls, "observation.img")
+    assert type(img).__name__ == "DummyImage"
+    assert img.arr.shape == (5, 6, 3)
+    assert _kwargs_for(calls, "observation.img").get("static", False) is True
+
+    # Vector logged as a single batched Scalars under one entity path
+    vec = _obj_for(calls, "action.vec")
+    assert type(vec).__name__ == "DummyScalar"
+    np.testing.assert_allclose(np.asarray(vec.value), [9, 8, 7])
+
+    # Blueprint sent once with the expected view layout
+    assert len(blueprints) == 1
+    bp = blueprints[0]
+    spatial_views = _views_by_kind(bp, "Spatial2DView")
+    assert {v.origin for v in spatial_views} == {"observation.img"}
+    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
+    assert ts_views["observation"].contents == ["observation.temp"]
+    assert ts_views["action"].contents == ["action.throttle", "action.vec"]
+
+
+def test_log_rerun_data_kwargs_only(mock_rerun):
+    rv, calls, blueprints = mock_rerun
+
+    rv.log_rerun_data(
+        observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)},
+        action={"action.a": 1.0},
+    )
+
+    keys = set(_keys(calls))
+    assert "observation.temp" in keys
+    assert "observation.gray" in keys
+    assert "action.a" in keys
+
+    temp = _obj_for(calls, "observation.temp")
+    assert type(temp).__name__ == "DummyScalar"
+    assert float(temp.value) == pytest.approx(10.0)
+
+    img = _obj_for(calls, "observation.gray")
+    assert type(img).__name__ == "DummyDepthImage"  # single-channel -> DepthImage
+    assert img.arr.shape == (8, 8, 1)  # remains HWC
+    assert _kwargs_for(calls, "observation.gray").get("static", False) is True
+
+    a = _obj_for(calls, "action.a")
+    assert type(a).__name__ == "DummyScalar"
+    assert float(a.value) == pytest.approx(1.0)
+
+    # Blueprint sent once, with a spatial view for the image and time-series views for scalars
+    assert len(blueprints) == 1
+    bp = blueprints[0]
+    assert {v.origin for v in _views_by_kind(bp, "Spatial2DView")} == {"observation.gray"}
+    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
+    assert ts_views["observation"].contents == ["observation.temp"]
+    assert ts_views["action"].contents == ["action.a"]
+
+
+def test_log_rerun_data_blueprint_sent_only_once(mock_rerun):
+    """The blueprint is built from the first call and not resent on subsequent calls."""
+    rv, calls, blueprints = mock_rerun
+
+    rv.log_rerun_data(observation={"temp": 1.0}, action={"a": 2.0})
+    assert len(blueprints) == 1
+    first_blueprint = rv.log_rerun_data.blueprint
+
+    rv.log_rerun_data(observation={"temp": 3.0}, action={"a": 4.0})
+    # Still only one blueprint, and the cached one is unchanged.
+    assert len(blueprints) == 1
+    assert rv.log_rerun_data.blueprint is first_blueprint

tests/utils/test_visualization_utils.py

@@ -14,297 +14,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import importlib
-import sys
-from types import SimpleNamespace
+"""Tests for the backend-agnostic visualization dispatch.
+
+These exercise the display-mode routing/validation only; they need neither ``rerun`` nor
+``foxglove`` installed since the unknown-mode branch raises before touching any backend. Backend
+behavior is covered in ``test_rerun_visualization.py`` and ``test_foxglove_visualization.py``.
+"""
 
-import numpy as np
 import pytest
 
-pytest.importorskip("rerun", reason="rerun-sdk is required (install lerobot[viz])")
-
-from lerobot.types import TransitionKey
-from lerobot.utils.constants import OBS_STATE
+from lerobot.utils import visualization_utils as vu
 
 
-@pytest.fixture
-def mock_rerun(monkeypatch):
-    """
-    Provide a mock `rerun` module (and `rerun.blueprint` submodule) so tests don't
-    depend on the real library. Also reload the module-under-test so it binds to
-    this mock `rr`.
-    """
-    calls = []
-    blueprints = []
-
-    class DummyScalar:
-        def __init__(self, value):
-            # Scalars may be built from a single float or from a 1D array batch.
-            self.value = value
-
-    class DummyImage:
-        def __init__(self, arr):
-            self.arr = arr
-
-        def compress(self, *a, **k):
-            return self
-
-    class DummyDepthImage:
-        def __init__(self, arr, colormap=None):
-            self.arr = arr
-            self.colormap = colormap
-
-    def dummy_log(key, obj=None, **kwargs):
-        # Accept either positional `obj` or keyword `entity` and record remaining kwargs.
-        if obj is None and "entity" in kwargs:
-            obj = kwargs.pop("entity")
-        calls.append((key, obj, kwargs))
-
-    def dummy_send_blueprint(blueprint, *a, **k):
-        blueprints.append(blueprint)
-
-    # Mock the `rerun.blueprint` submodule used to build the layout.
-    dummy_rrb = SimpleNamespace(
-        Spatial2DView=lambda origin=None, name=None: SimpleNamespace(
-            kind="Spatial2DView", origin=origin, name=name
-        ),
-        TimeSeriesView=lambda name=None, contents=None: SimpleNamespace(
-            kind="TimeSeriesView", name=name, contents=contents
-        ),
-        Grid=lambda *views: SimpleNamespace(kind="Grid", views=list(views)),
-        Blueprint=lambda root: SimpleNamespace(kind="Blueprint", root=root),
-    )
-
-    dummy_rr = SimpleNamespace(
-        __name__="rerun",
-        __package__="rerun",
-        __spec__=SimpleNamespace(name="rerun", submodule_search_locations=None),
-        Scalars=DummyScalar,
-        Image=DummyImage,
-        DepthImage=DummyDepthImage,
-        components=SimpleNamespace(Colormap=SimpleNamespace(Viridis="viridis")),
-        log=dummy_log,
-        send_blueprint=dummy_send_blueprint,
-        init=lambda *a, **k: None,
-        spawn=lambda *a, **k: None,
-        blueprint=dummy_rrb,
-    )
-
-    # Inject fake modules into sys.modules (both `rerun` and `rerun.blueprint`).
-    monkeypatch.setitem(sys.modules, "rerun", dummy_rr)
-    monkeypatch.setitem(sys.modules, "rerun.blueprint", dummy_rrb)
-
-    # Now import and reload the module under test, to bind to our rerun mock
-    import lerobot.utils.visualization_utils as vu
-
-    importlib.reload(vu)
-
-    # Expose the reloaded module, the call recorder and the captured blueprints
-    yield vu, calls, blueprints
+def test_visualization_modes():
+    assert vu.VISUALIZATION_MODES == ("rerun", "foxglove")
 
 
-def _keys(calls):
-    """Helper to extract just the keys logged to rr.log"""
-    return [k for (k, _obj, _kw) in calls]
-
-
-def _obj_for(calls, key):
-    """Find the first object logged under a given key."""
-    for k, obj, _kw in calls:
-        if k == key:
-            return obj
-    raise KeyError(f"Key {key} not found in calls: {calls}")
-
-
-def _kwargs_for(calls, key):
-    for k, _obj, kw in calls:
-        if k == key:
-            return kw
-    raise KeyError(f"Key {key} not found in calls: {calls}")
-
-
-def _views_by_kind(blueprint, kind):
-    """Return the views of a given kind from the (single) blueprint's grid."""
-    return [v for v in blueprint.root.views if v.kind == kind]
-
-
-def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
-    vu, calls, blueprints = mock_rerun
-
-    # Build EnvTransition dict
-    obs = {
-        f"{OBS_STATE}.temperature": np.float32(25.0),
-        # CHW image should be converted to HWC for rr.Image
-        "observation.camera": np.zeros((3, 10, 20), dtype=np.uint8),
-    }
-    act = {
-        "action.throttle": 0.7,
-        # 1D array should be logged as a single Scalars batch under one entity path
-        "action.vector": np.array([1.0, 2.0], dtype=np.float32),
-    }
-    transition = {
-        TransitionKey.OBSERVATION: obs,
-        TransitionKey.ACTION: act,
-    }
-
-    # Extract observation and action data from transition like in the real call sites
-    obs_data = transition.get(TransitionKey.OBSERVATION, {})
-    action_data = transition.get(TransitionKey.ACTION, {})
-    vu.log_rerun_data(observation=obs_data, action=action_data)
-
-    # We expect:
-    # - observation.state.temperature -> Scalars
-    # - observation.camera -> Image (HWC) with static=True
-    # - action.throttle -> Scalars
-    # - action.vector -> single Scalars batch (no per-element suffix)
-    expected_keys = {
-        f"{OBS_STATE}.temperature",
-        "observation.camera",
-        "action.throttle",
-        "action.vector",
-    }
-    assert set(_keys(calls)) == expected_keys
-
-    # Check scalar types and values
-    temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature")
-    assert type(temp_obj).__name__ == "DummyScalar"
-    assert float(temp_obj.value) == pytest.approx(25.0)
-
-    throttle_obj = _obj_for(calls, "action.throttle")
-    assert type(throttle_obj).__name__ == "DummyScalar"
-    assert float(throttle_obj.value) == pytest.approx(0.7)
-
-    # 1D vector logged as a single batched Scalars under one entity path
-    vec = _obj_for(calls, "action.vector")
-    assert type(vec).__name__ == "DummyScalar"
-    np.testing.assert_allclose(np.asarray(vec.value), [1.0, 2.0])
-
-    # Check image handling: CHW -> HWC
-    img_obj = _obj_for(calls, "observation.camera")
-    assert type(img_obj).__name__ == "DummyImage"
-    assert img_obj.arr.shape == (10, 20, 3)  # transposed
-    assert _kwargs_for(calls, "observation.camera").get("static", False) is True  # static=True for images
-
-    # A blueprint should have been built and sent exactly once, and cached on the function.
-    assert len(blueprints) == 1
-    assert vu.log_rerun_data.blueprint is blueprints[0]
-
-    bp = blueprints[0]
-    # One spatial view per image path
-    spatial_views = _views_by_kind(bp, "Spatial2DView")
-    assert {v.origin for v in spatial_views} == {"observation.camera"}
-
-    # One time-series view each for observation and action scalars
-    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
-    assert set(ts_views) == {"observation", "action"}
-    assert ts_views["observation"].contents == [f"{OBS_STATE}.temperature"]
-    assert ts_views["action"].contents == ["action.throttle", "action.vector"]
-
-
-def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
-    vu, calls, blueprints = mock_rerun
-
-    # First dict without prefixes treated as observation
-    # Second dict without prefixes treated as action
-    obs_plain = {
-        "temp": 1.5,
-        # Already HWC image => should stay as-is
-        "img": np.zeros((5, 6, 3), dtype=np.uint8),
-        "none": None,  # should be skipped
-    }
-    act_plain = {
-        "throttle": 0.3,
-        "vec": np.array([9, 8, 7], dtype=np.float32),
-    }
-
-    # Extract observation and action data from list like the old function logic did
-    # First dict was treated as observation, second as action
-    vu.log_rerun_data(observation=obs_plain, action=act_plain)
-
-    # Expected keys with auto-prefixes. The 1D vector is a single batched Scalars.
-    expected = {
-        "observation.temp",
-        "observation.img",
-        "action.throttle",
-        "action.vec",
-    }
-    logged = set(_keys(calls))
-    assert logged == expected
-
-    # Scalars
-    t = _obj_for(calls, "observation.temp")
-    assert type(t).__name__ == "DummyScalar"
-    assert float(t.value) == pytest.approx(1.5)
-
-    throttle = _obj_for(calls, "action.throttle")
-    assert type(throttle).__name__ == "DummyScalar"
-    assert float(throttle.value) == pytest.approx(0.3)
-
-    # Image stays HWC
-    img = _obj_for(calls, "observation.img")
-    assert type(img).__name__ == "DummyImage"
-    assert img.arr.shape == (5, 6, 3)
-    assert _kwargs_for(calls, "observation.img").get("static", False) is True
-
-    # Vector logged as a single batched Scalars under one entity path
-    vec = _obj_for(calls, "action.vec")
-    assert type(vec).__name__ == "DummyScalar"
-    np.testing.assert_allclose(np.asarray(vec.value), [9, 8, 7])
-
-    # Blueprint sent once with the expected view layout
-    assert len(blueprints) == 1
-    bp = blueprints[0]
-    spatial_views = _views_by_kind(bp, "Spatial2DView")
-    assert {v.origin for v in spatial_views} == {"observation.img"}
-    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
-    assert ts_views["observation"].contents == ["observation.temp"]
-    assert ts_views["action"].contents == ["action.throttle", "action.vec"]
-
-
-def test_log_rerun_data_kwargs_only(mock_rerun):
-    vu, calls, blueprints = mock_rerun
-
-    vu.log_rerun_data(
-        observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)},
-        action={"action.a": 1.0},
-    )
-
-    keys = set(_keys(calls))
-    assert "observation.temp" in keys
-    assert "observation.gray" in keys
-    assert "action.a" in keys
-
-    temp = _obj_for(calls, "observation.temp")
-    assert type(temp).__name__ == "DummyScalar"
-    assert float(temp.value) == pytest.approx(10.0)
-
-    img = _obj_for(calls, "observation.gray")
-    assert type(img).__name__ == "DummyDepthImage"  # single-channel -> DepthImage
-    assert img.arr.shape == (8, 8, 1)  # remains HWC
-    assert _kwargs_for(calls, "observation.gray").get("static", False) is True
-
-    a = _obj_for(calls, "action.a")
-    assert type(a).__name__ == "DummyScalar"
-    assert float(a.value) == pytest.approx(1.0)
-
-    # Blueprint sent once, with a spatial view for the image and time-series views for scalars
-    assert len(blueprints) == 1
-    bp = blueprints[0]
-    assert {v.origin for v in _views_by_kind(bp, "Spatial2DView")} == {"observation.gray"}
-    ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
-    assert ts_views["observation"].contents == ["observation.temp"]
-    assert ts_views["action"].contents == ["action.a"]
-
-
-def test_log_rerun_data_blueprint_sent_only_once(mock_rerun):
-    """The blueprint is built from the first call and not resent on subsequent calls."""
-    vu, calls, blueprints = mock_rerun
-
-    vu.log_rerun_data(observation={"temp": 1.0}, action={"a": 2.0})
-    assert len(blueprints) == 1
-    first_blueprint = vu.log_rerun_data.blueprint
-
-    vu.log_rerun_data(observation={"temp": 3.0}, action={"a": 4.0})
-    # Still only one blueprint, and the cached one is unchanged.
-    assert len(blueprints) == 1
-    assert vu.log_rerun_data.blueprint is first_blueprint
+@pytest.mark.parametrize("func", ["init_visualization", "log_visualization_data", "shutdown_visualization"])
+def test_dispatch_rejects_unknown_mode(func):
+    with pytest.raises(ValueError, match="Unknown display_mode"):
+        getattr(vu, func)("bogus")

uv.lock

@@ -1550,6 +1550,26 @@ wheels = [
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 ]
 
+[[package]]
+name = "foxglove-sdk"
+version = "0.25.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c1/a7/86a252782ea0d9baf1357369ad1bbf1ed644768702b0266a3fa3a05361d0/foxglove_sdk-0.25.1.tar.gz", hash = "sha256:8230f3c32ea3ab715818687377491594ec9c7e58e6b0ed8ed91aadf937ce706b", size = 547778, upload-time = "2026-06-02T03:13:18.942Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/27/ed/16d809fab24cbfdf97c15c9cdd80eabfeb447ca545ede426950d62bac848/foxglove_sdk-0.25.1-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:3e908bd87d1926a05c785779d8252db6b87eef685f284ec1cf46ee501645d08e", size = 16452309, upload-time = "2026-06-02T03:13:10.607Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/c3/f95874935a3436841487df1f0202de4d20eabc0adb6b79c94c531bbe7eb3/foxglove_sdk-0.25.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:968e32c8668d172f6b546c8e7af658ed35a21ec165adc3bacf53a04dda159f12", size = 2355680, upload-time = "2026-06-02T02:34:01.668Z" },
+    { url = "https://files.pythonhosted.org/packages/38/da/ad22d8d6e3fedde9fc0c49aa8b20394e5e0bc44ab3fba564c77a64ddc7e2/foxglove_sdk-0.25.1-cp310-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3f75374fedafe259c40b19bc645589d9453708eab679a5b07c603035f936d29a", size = 2274075, upload-time = "2026-06-02T02:34:07.212Z" },
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+    { url = "https://files.pythonhosted.org/packages/5f/4d/bdb9e252a41a951eb53908ac9cb965b7480c3ba649174f5398d4fcf0ca1d/foxglove_sdk-0.25.1-cp310-abi3-win_amd64.whl", hash = "sha256:6ed3ad0d3e72cd7875e7e293709c5ff90494fe14f1b48a336baffc313a7272cc", size = 16588452, upload-time = "2026-06-02T03:13:21.636Z" },
+]
+
 [[package]]
 name = "fqdn"
 version = "1.5.1"
@@ -2811,6 +2831,7 @@ all = [
     { name = "faker" },
     { name = "fastapi" },
     { name = "feetech-servo-sdk" },
+    { name = "foxglove-sdk" },
     { name = "grpcio" },
     { name = "grpcio-tools" },
     { name = "gym-aloha" },
@@ -2895,6 +2916,7 @@ core-scripts = [
     { name = "av" },
     { name = "datasets" },
     { name = "deepdiff" },
+    { name = "foxglove-sdk" },
     { name = "jsonlines" },
     { name = "pandas" },
     { name = "pyarrow" },
@@ -2917,6 +2939,7 @@ dataset = [
 dataset-viz = [
     { name = "av" },
     { name = "datasets" },
+    { name = "foxglove-sdk" },
     { name = "jsonlines" },
     { name = "pandas" },
     { name = "pyarrow" },
@@ -3187,6 +3210,7 @@ video-benchmark = [
     { name = "scikit-image" },
 ]
 viz = [
+    { name = "foxglove-sdk" },
     { name = "rerun-sdk" },
 ]
 vla-jepa = [
@@ -3226,6 +3250,7 @@ requires-dist = [
     { name = "fastapi", marker = "extra == 'phone'", specifier = "<1.0" },
     { name = "feetech-servo-sdk", marker = "extra == 'feetech'", specifier = ">=1.0.0,<2.0.0" },
     { name = "flash-attn", marker = "sys_platform != 'darwin' and extra == 'groot'", specifier = ">=2.5.9,<3.0.0" },
+    { name = "foxglove-sdk", marker = "extra == 'viz'", specifier = ">=0.25.1,<0.26.0" },
     { name = "grpcio", marker = "extra == 'grpcio-dep'", specifier = ">=1.73.1,<2.0.0" },
     { name = "grpcio", marker = "extra == 'reachy2'", specifier = "<=1.73.1" },
     { name = "grpcio-tools", marker = "extra == 'dev'", specifier = ">=1.73.1,<2.0.0" },