* fix(envs): set LiberoEnvConfig.fps default to 20 to match robosuite
LiberoEnvConfig.fps was set to 30, but the underlying robosuite
OffScreenRenderEnv always runs at its default control_freq of 20 Hz
since fps is never passed through. This mismatch silently decouples
the dataset/eval loop rate from the actual simulation step rate.
Set the default to 20 to match the real sim rate and avoid the
footgun.
Fixes#3368
* fix(libero): apply configured control frequency
---------
Co-authored-by: xinmotlanthua <275663218+xinmotlanthua@users.noreply.github.com>
* fix(damiao): make is_calibrated a plain property, not cached
`is_calibrated` was a `@cached_property`, so it froze at its first-read
value and never reflected later changes to `self.calibration` (set by
connect/calibrate/load). This caused the OpenArm teleop to re-run
calibration even when a calibration file existed, and to skip
`set_zero_position()` after a fresh calibration.
Switch to `@property` (matching the MotorsBus base contract and the
Feetech/SO-100 buses) and drop the now-unused `functools.cached_property`
import.
Co-authored-by: Cursor <cursoragent@cursor.com>
* don't set_zero_position() on connect
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(diffusion): add gradient checkpointing for memory optimization
Add gradient_checkpointing config option to DiffusionPolicy. When
enabled, wraps the UNet encoder, mid, and decoder residual blocks
with torch.utils.checkpoint.checkpoint to trade compute for memory.
Allows training with larger batch sizes or higher-resolution inputs
on memory-constrained GPUs. Disabled by default.
Usage: --policy.gradient_checkpointing=true
Part of the 0.6.0 roadmap item 3.3 (gradient checkpointing for all
policies).
* test(diffusion): verify gradient checkpointing parity
---------
Co-authored-by: Jash Shah <jashshah.999@gmail.com>
* gamepad does often work on macos
* review comments
* fix(gamepad): expose hidapi fallback in config
---------
Co-authored-by: Maxim Bonnaerens <maxim@bonnaerens.be>
* feat(annotate): run lerobot-annotate on HF Jobs via --job.target
Annotation needed a hand-edited launcher script (examples/annotations/run_hf_job.py)
to reach a GPU: users copied it, rewrote the embedded CMD string for their dataset,
and ran it with `python`. Fold that into the CLI instead, mirroring `lerobot-train`:
`lerobot-annotate --job.target=h200` submits the exact command you'd run locally.
- AnnotationJobConfig extends JobConfig with the annotation runtime's defaults
(vllm/vllm-openai image, 2h cap) plus --job.lerobot_ref, so an unmerged branch
can be exercised remotely without editing a script.
- lerobot.jobs.annotate builds the pod command by replaying the user's own CLI
flags (minus --job.*/--root, with --repo_id re-emitted from the config) after a
setup prelude that installs lerobot on top of the vLLM image. Job monitoring,
log tailing and Ctrl-C-detaches reuse the training submitter's plumbing.
- Remote runs require --repo_id; a local-only dataset is pushed privately first.
The generated pod command is byte-for-byte the script's old CMD.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(annotate): reject client-side config files on remote runs
draccus exposes `--config_path` plus a `--<field>` config-file arg for every
nested dataclass (`--vlm`, `--plan`, `--job`, ...). All name files on the
client's disk, so forwarding them to the pod silently dropped whatever settings
they carried. Reject them up front instead.
Bare `--job` also slipped past the `--job.` prefix filter, so a `--job=cfg.yaml`
holding `target: h200` would have reached the pod and had the job submit a job
of its own, recursively. It is dropped from the forwarded args as well.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* refactor(jobs): share the submit-and-follow loop between both submitters
`submit_annotate_to_hf` reused the leaf helpers (`_poll_until_done`, `_tail_logs`,
`_pod_forwarded_args`) but duplicated the orchestration around them: ~40 of the 50
lines that spawn the poll/log threads, install the Ctrl-C-detaches handler and
raise on a non-COMPLETED stage were identical in both files.
Extract that into `follow_job(job_id, *, detach, success_marker=None) -> bool`,
returning True when the job finished and False when we stopped watching without a
verdict (detach or Ctrl-C). Training keeps its model-pushed marker by passing it in;
annotation has no equivalent line (the CLI keeps working after the upload log to
write the card and tag) so its completion stays stage-based.
Kept in hf.py rather than a new module so every existing monkeypatch target in
test_hf.py still resolves.
Behaviour change: a training run whose job reaches COMPLETED without the marker
matching now prints its completion line instead of returning silently. The marker
was already documented as an optimisation with a stage-based fallback; the fallback
just never reported success.
Tests: adds annotate coverage for the non-detach path (completion and failure) —
previously only ever exercised with detach=true — plus a detach short-circuit test.
Both new annotate tests verified to fail under a mutation that stubs out follow_job.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* feat(annotate): WGO-tuned subtask prompt (atomic completed-events + duration prior)
Rework the plan-module subtask segmentation prompt toward the WGO-Bench
atomic annotation protocol: segment by completed world-state changes
(grasp/place/open/close/pour/insert), fold approach+retreat into their
event, keep separate events separate, and add a 2-10s duration prior.
Drops the pi0.7 "fewer larger composites preferred" bias that drove
under-segmentation on the benchmark. Output JSON shape unchanged.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(annotate): seeded-relabeling second pass for subtasks
Add an opt-in relabel pass (plan.subtask_seeded_relabel) that, after
segmentation, re-labels each span using previous/current/next segment
contact sheets and the seed label as a strong prior, minimally correcting
it. Mirrors macrodata's best end-to-end labeling step. Boundaries are
untouched; one extra VLM call per span. Off by default.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(annotate): robust OpenAI-compat client for hosted VLMs
Guard against a choice with no message (safety filter or a thinking model
that spends its whole budget before emitting content) so one empty reply
no longer crashes the whole annotation run; treat it as an empty response
and let the existing JSON-retry path handle it.
Add an optional `reasoning_effort` knob on VlmConfig, forwarded to the
server when set, to cap a thinking model's reasoning (needed for Gemini
via its OpenAI-compatible endpoint).
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(annotate): legible tile-scaled timestamp on contact sheets
The burned-in timestamp used the ~10px bitmap default font, which blurs
once the model downsamples a full contact sheet into 768px tiles, so the
VLM can no longer read the exact source time a boundary depends on. Scale
the timestamp to the tile height (with a graceful fallback on older
Pillow) so the visual time cue stays readable at sheet resolution.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(annotate): lean GEPA-aligned subtask segmentation prompt
Replace the verbose, label-heavy segmentation prompt with a lean
adaptation of the blog's GEPA-found completed_events_duration_prior
recipe: focus on completed manipulation events, explicit no-split /
no-merge rules, a 2-10s duration prior, and an instruction to prioritize
temporally correct boundaries over label wording. The previous prompt
over-weighted label guidance, which traded away boundary precision.
Co-authored-by: Cursor <cursoragent@cursor.com>
* revert: restore original subtask segmentation prompt
The lean GEPA-aligned paraphrase (dd4b0110d) regressed Gemini on the
30-ep subset: Seg F1 0.259 -> 0.189 and E2E 0.184 -> 0.135, driven by
worse under-segmentation (224 -> 188 preds). The blog's 0.306 came from
the actual GEPA-search artifact, which a hand paraphrase does not
reproduce. Restore the original prompt, which remains our best config.
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(annotate): env-var override for prompt templates
Allow LEROBOT_PROMPT_OVERRIDE_<name> to supersede the packaged prompt
file at load time. Enables prompt search (GEPA) to inject candidate
segmentation prompts into a remote annotate job via an env secret,
without committing a branch per candidate.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(annotate): genericize hosted-VLM comments (no model name)
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(annotate): document seeded-relabel and reasoning_effort flags
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(annotate): update subtask-prompt marker to match WGO-tuned prompt
The three plan-module tests keyed the canned VLM responder on the
literal 'atomic subtasks', which the WGO-tuned segmentation prompt no
longer contains (it now segments 'COMPLETED manipulation events'). Point
the fixture markers at the current wording so the subtask call is matched
again.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* Add Isaac Teleop SO-101 leader-arm teleoperator
Add the NVIDIA Isaac Teleop teleoperator scaffolding and its first device:
SO101LeaderArm, a back-drivable SO-101 leader arm on Isaac Teleop's generic
joint-space device path. It reads the leader's joints from the so101_leader
plugin via JointStateSource and emits follower-ready {joint}.pos (rad2deg arm,
gripper -> RANGE_0_100) for direct 1:1 joint drive.
- IsaacTeleopTeleoperator base + IsaacTeleopConfig (shared session/CloudXR config)
- SO101LeaderArm / SO101LeaderArmConfig and leader_joints_to_robot_action
- examples/isaac_teleop_to_so101/teleoperate_leader.py example
- pure-numpy conversion tests
- isaac-teleop optional extra + NVIDIA PyPI index in pyproject
* Add Isaac Teleop XR controller teleoperator for SO-101
Add end-to-end XR (VR) controller teleoperation of an SO-101 follower arm via
the NVIDIA Isaac Teleop stack, layered on the Isaac Teleop scaffolding.
Teleoperator (src/lerobot/teleoperators/isaac_teleop/):
- XRController / XRControllerConfig: connect to the CloudXR runtime, auto-launch
the Isaac Teleop session, and expose get_action() emitting the raw base-frame
grip pose, squeeze, and trigger.
- MapXRControllerActionToRobotAction: stateless per-frame mapper from the XR
action to the IK input contract (absolute ee.x/y/z, ee.gripper_pos, wrist_roll).
- OverwriteWristRollFromAngle: post-IK step writing the operator wrist-roll [rad]
onto wrist_roll.pos [deg], recovering the under-determined roll DOF.
Example (examples/isaac_teleop_to_so101/):
- teleoperate.py: thin absolute-pose IK pipeline with an in-loop clutch (engage
latch + 1:1 delta rebase of position and orientation), EEBoundsAndSafety, and
InverseKinematicsEEToJoints; slews to a recorded home on startup.
- record_reset_pose.py / download_assets.py / webxr.env / .gitignore.
Also:
- Extend robot_kinematic_processor.py with EEBoundsAndSafety and
InverseKinematicsEEToJoints.
- Add XRControllerConfig + base_T_anchor to the Isaac Teleop config.
- Add docs/source/isaac_teleop.mdx and the _toctree entry.
- Add unit tests for the CloudXR launcher and the XR controller processor.
* Unify Isaac Teleop SO-101 scripts behind a mandatory device selector
Merge teleoperate.py (XR controller: clutch + soft-orientation IK) and
teleoperate_leader.py (SO-101 leader arm: 1:1 joint mirror) into a single
teleoperate.py driven by a `lerobot-teleoperate`-style draccus CLI: a follower
`--robot.*` and an input `--teleop.*`, where `--teleop.type` (xr_controller |
so101_leader) selects the Isaac device.
Uses a "dispatch, don't merge" shape: per-device setup_xr/setup_leader build a
Device bundle (compute / startup / cleanup / command); a shared slew() takes a
per-step target callable (XR a fixed reset pose, leader a live re-read so the
1:1 handoff stays continuous); one device-branchless outer loop runs both, with
compute() -> None meaning "hold at the measured pose" (XR disengaged or leader
stale). The entrypoint is @parser.wrap()'d over a TeleoperateConfig dataclass and
dispatches on the parsed config type; device knobs ride on --teleop.* (the leader
serial port is --teleop.port, forwarded to the plugin) and loop/launch knobs are
top-level (--launch_plugin=<path> collapses the old --launch-plugin/--plugin-bin
pair; --reset_to_origin/--align/--dry_run).
To let the Isaac devices claim the natural --teleop.type names without colliding
with the serial so101_leader of lerobot-teleoperate, give IsaacTeleopConfig its
own draccus choice registry (own _choice_registry, decoupled from the global
TeleoperatorConfig one) and register XRControllerConfig as "xr_controller" and
SO101LeaderArmConfig as "so101_leader" there; the example types its teleop field
as IsaacTeleopConfig so the choices resolve against that scoped registry. These
devices drive the bespoke clutch/IK/align loop and are not routed through
make_teleoperator_from_config, so dropping them from the global registry is inert.
YAGNI sweep of the commit train: delete the orphaned OverwriteWristRollFromAngle
(wrist_roll_processor.py) plus its export and tests -- no producer emits
wrist_roll; the live XR path uses orientation-weight IK on the 5-DOF arm by
design. Kept the load-bearing knobs (orientation_weight, raise_on_jump,
base_T_anchor) and the optional reset-pose recorder. Updated isaac_teleop.mdx
for the unified entrypoint and excised the stale roll-retargeter prose.
Net LOC down (two scripts 714 lines -> one), in-loop device branches reduced to
zero. Planned and reviewed via a 6-persona multi-agent panel (3-round planning
convergence + 2-round review). Verification (isaacteleop/placo not installable
here, so the device classes cannot connect, but their config dataclasses and the
script import fine via deferred imports): the teleoperators test suite passes
(45 passed, 2 skipped), draccus parsing of both target command lines yields the
right config subclass with scoped --teleop.type, --help renders the scoped
choices, the serial so101_leader stays in the global registry, and ruff
check/format are green.
Signed-off-by: Jiwen Cai <jiwenc@nvidia.com>
* Add Isaac Teleop SO-101 dataset recording script
record.py records a LeRobot dataset while driving the SO-101 follower
with either Isaac Teleop device (--teleop.type=xr_controller |
so101_leader), mirroring teleoperate.py's device dispatch.
* Extract shared Isaac Teleop SO-101 example infra into common.py
teleoperate.py and record.py both built the per-device pipeline and ran the
same read -> compute -> hold-when-idle -> sleep loop, with record.py importing
internals from teleoperate.py. Move the shared device/loop infrastructure
(Device, slew, Clutch, setup_xr/setup_leader + leader helpers, reset infra and
constants) into a new common.py, and add build_device() + hold_action() to
collapse the connect/dispatch/startup and idle-hold glue duplicated in both
entry points. The setup functions now type their config against a LoopConfig
Protocol, so common.py is decoupled from either CLI; both import from it.
Also rename record_reset_pose.py -> override_reset_pose.py so it is not confused
with record.py, and update the doc references.
* Add stdin keyboard backend so recording shortcuts work over SSH/headless
lerobot's init_keyboard_listener() uses pynput, which hooks GLOBAL key events
from the display server. Over SSH, under Wayland, or on a headless box with only a
TTY, keystrokes go to the terminal's stdin instead, so the listener never fires and
the Right/Left/Esc recording shortcuts silently do nothing.
Add a stdin (termios) keyboard backend to the example's common.py and an
init_keyboard_listener() that prefers it whenever stdin is an interactive TTY
(works over SSH / Wayland / headless-with-tty), falling back to lerobot's
pynput/headless listener for GUI launches with no controlling terminal. Selectable
via LEROBOT_KEYBOARD_BACKEND={auto,stdin,pynput,none}. The backend keeps ISIG so
Ctrl-C still works and always restores the terminal (on stop() and via atexit).
record.py now sources init_keyboard_listener from common; the Right/Left/Esc -> flag
mapping and the (listener, events) contract are unchanged.
Also convert record.py's loop_kwargs to a dict literal (ruff C408).
* Wait for the XR headset to connect before driving the arm
On the xr_controller path the example connected CloudXR and immediately ran the
reset slew + control loop, even if no headset was connected — the arm moved before
the operator was in VR, and get_action() just returned zeros so the clutch never
engaged.
Add an is_tracking property to XRController (set from the controller stream's
optional group, mirroring SO101LeaderArm) and a _wait_for_xr_controller() helper in
common.py that prints connection instructions (CloudXR web client URL + this
workstation's candidate IPv4s, with loopback/link-local and virtual/bridge/USB-gadget
interfaces filtered out) and polls until the controllers stream (indefinite, 15s
reminder, Ctrl-C to abort). setup_xr.startup() now connects, waits for the headset,
THEN runs the reset slew and seeds the clutch — so the arm only moves once the
operator is connected and watching. Mirrors the leader path's _wait_for_leader; both
record.py and teleoperate.py inherit it via the shared setup_xr.
* Address review feedback on the Isaac Teleop -> SO-101 example
Review-response and CI fixes for the Isaac Teleop -> SO-101 example.
- Move the XR Clutch into src/lerobot/teleoperators/isaac_teleop/clutch.py
(pure numpy + Rotation, no isaacteleop import), export it, and add
tests/teleoperators/test_clutch.py.
- Drop the vendored stdin keyboard listener; record.py uses a small terminal-
first wrapper over upstream's TerminalKeyListener (works over SSH even with a
local X display), falling back to upstream init_keyboard_listener otherwise.
- record.py: pass rgb_encoder/depth_encoder to LeRobotDataset create()/resume()
(upstream removed camera_encoder), fixing the AttributeError at record time.
- build_device: derive motor names from robot.action_features instead of
robot.bus (supports non-bus robots), and disconnect the follower if any step
after connect() fails so a failed setup never leaks the connection.
- Read leader joints by the group's declared names (_joints_group_to_rad)
instead of positionally, so a layout mismatch can't silently mirror the wrong
DOF onto the follower; add tests including a reversed-layout group.
- base.py: hoist `from pathlib import Path` to module scope; only the
isaacteleop CloudXRLauncher import stays lazy (optional dep).
- Trim the common.py module docstring and point to docs/source/isaac_teleop.mdx.
- default.env: correct the NV_DEVICE_PROFILE comment (auto-webrtc is the default;
this file overrides to Quest3, which works for both Quest 3 and Pico 4).
- download_assets.py: correct the RAW_BASE comment (tracks main, not pinned) and
add `# nosec B310` next to the existing `# noqa: S310` for the bandit hook.
- uv.lock: add the isaac-teleop extra's deps so `uv sync --locked` matches
pyproject; regenerated with uv 0.8.0 to keep lockfile revision 2 (CI's uv).
- isaac_teleop.mdx: prettier formatting.
* fix(.gitignore): removing .gitignore and using lerobot cache folder instead to store local user files
* chore(docstrings): reducing docstrings in default.env
* feat(URDF): cleaning up and simplifying the URDF download procedure
* feat(robot guard): adding a guard in case an unsupported robot type is provided (so-arms only)
* fix(imports): enforcing a python module structure to simplify imports
* feat(safe read): extending the motor bus safe read rationale to reset pose setting
* chore(trim): trimming lenghty comments and docstrings
* fix(deps): use isaacteleop [retargeters-lite] extra to unblock aarch64 (DGX Spark) (#3933)
* fix(deps): drop isaacteleop [retargeters] extra to unblock aarch64
The [retargeters] extra pulls dex-retargeting (pins numpy<2.0, conflicting
with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels), making
lerobot[isaac-teleop] unresolvable on ARM (DGX Spark, Jetson Thor, GH200)
and over-constrained on numpy everywhere else.
The LeRobot teleoperators only import isaacteleop.retargeting_engine,
isaacteleop.cloudxr and isaacteleop.teleop_session_manager, all shipped in
the base wheel (requires only numpy>=1.23), so the extra is unused.
Verified on DGX Spark (aarch64, Python 3.12): resolves and installs with
isaacteleop 1.3.131 + numpy 2.2.6; all imported symbols load.
* fix(deps): use isaacteleop [retargeters-lite] extra for aarch64 support
Pin to isaacteleop ~=1.3.131 (the release that added ARM64/aarch64 support)
and swap the full [retargeters] extra for the new [retargeters-lite] one
(scipy-only). The full extra drags in dex-retargeting (pins numpy<2,
conflicting with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels),
making lerobot[isaac-teleop] unresolvable on ARM hosts (DGX Spark, Jetson
Thor, GH200) and over-constrained on numpy everywhere else.
The LeRobot teleoperators only import isaacteleop.retargeting_engine,
isaacteleop.cloudxr and isaacteleop.teleop_session_manager — all covered
by the base wheel + retargeters-lite.
Verified on DGX Spark (aarch64, Python 3.12/3.13): resolves and installs
with isaacteleop 1.3.131 + numpy 2.2.6 + scipy 1.18.
* feat(deps): re-add full [retargeters] extra gated to x86_64
Keep the dex-retargeting/nlopt-based retargeters available on x86_64 (where
their wheels exist) via an environment marker, while ARM hosts (DGX Spark,
Jetson Thor, GH200) resolve with base + [retargeters-lite] only.
Verified: uv lock resolves on both platforms; on aarch64 the compile
excludes nlopt/dex-retargeting, on x86_64 they are included.
---------
Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com>
* chore(docstrings): trimming latest docstrings
* chore(teleop): move isaac-teleop to examples + update docs + add readme with installation notes
* chore(deps): restore uv.lock
* fix(example: isaac teleop parsing config
* fix(examples): isaac atomic-gripper controller
* feat(Examples): isaac-teleop holdlatch
* chore(examples): some other minor improvements for isaac-teleop
* chore(examples): top-level imports isaac-teleop
* chore(Examples): address ai review isaac-teleop
---------
Signed-off-by: Jiwen Cai <jiwenc@nvidia.com>
Co-authored-by: Jiwen Cai <jiwenc@nvidia.com>
Co-authored-by: Johnny <johnnync13@gmail.com>
Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(policies): add EVO1 policy
* fix(evo1): infer batch size after normalizing image dims
`_collect_image_batches` read `batch_size = batch[camera_keys[0]].shape[0]`
before normalizing per-camera tensors to `(B, C, H, W)`. For an unbatched
`(C, H, W)` input (which the function tries to support via the `image.dim() == 3`
branch), this picked up the channel count `C` instead of the real batch size,
making the subsequent per-sample loop iterate `C` times and indexing go
out of bounds.
Normalize each camera tensor up-front, then read `batch_size` from the
normalized batch dim. Adds `test_collect_image_batches_handles_unbatched_chw`
covering the regression.
Reported by Copilot review on huggingface/lerobot#3545.
* chore(lock): regenerate uv.lock for evo1 extra
Adds the `evo1` entry to `[package.metadata.requires-dist]` and the
`provides-extras` list so that `uv sync --locked --extra test` (used by
fast_tests.yml) no longer reports the lockfile as stale.
Generated with `uv 0.8.0` (matching `UV_VERSION` in fast_tests.yml).
The non-evo1 marker tweaks are produced by `uv lock` re-resolving the
existing dep graph and are not introduced by this PR.
* chore(evo1): align with policy contribution guide conventions
- Add `src/lerobot/policies/evo1/README.md` symlink into `docs/source/evo1.mdx`
to match the in-tree README convention (mirroring the EO-1 layout).
- Convert `transformers` import in `internvl3_embedder.py` to the standard
`TYPE_CHECKING + _transformers_available` two-step gating used by other
optional-backbone policies (e.g. diffusion). The previous lazy-in-`__init__`
import was functionally equivalent for runtime gating but didn't expose the
real symbols to type checkers.
- Add `lerobot[evo1]` to the `all` extra in `pyproject.toml` so
`pip install 'lerobot[all]'` keeps installing every optional policy.
Per the guidance in https://moon-ci-docs.huggingface.co/docs/lerobot/pr_3534/en/contributing_a_policy.
* fix(evo1): finalize policy guide alignment
* docs(evo1): format results table
* Fix EVO1 LIBERO rollout processors
* Fix EVO1 LIBERO eval action postprocessing
* Fix eval action conversion for bf16 policies
* fix(evo1): move LIBERO padding into policy processors
* refactor(evo1): use native HF InternVL3-1B-hf, drop trust_remote_code
- Switch from OpenGVLab/InternVL3-1B (requires trust_remote_code=True)
to OpenGVLab/InternVL3-1B-hf (native transformers implementation).
- Replace manual _extract_feature + _prepare_and_fuse_embeddings with
a single model.forward() call — verified bit-for-bit identical output.
- Remove ~170 lines of manual ViT/pixel-shuffle/projection logic.
- Symlink README.md to docs/source/ following repo convention.
Weights are byte-identical between both model variants; only the module
naming differs. All 12 existing unit tests pass. Local training (10 steps)
on maximellerbach/omx_pickandplace confirmed working.
* refactor(policy): evo1 GPU-batched preprocessing + vectorized attention masking + remove dead code
* fix(style): pre-commit
oops
* chore(evo1): delete added test + reduce diff
* refactor(policies): use config for evo1 + local imports
* refactor(policies): multiple improvements
* chore: update docs + remove legacy codepaths
* feat(policies): implement RTC to EVO1
---------
Co-authored-by: javadcc_mac <javadcc1@sjtu.edu.cn>
Co-authored-by: Yiming Wang <145452074+JAVAdcc@users.noreply.github.com>
Co-authored-by: Martino Russi <nopyeps@gmail.com>
Pepijn