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22 Commits

Author SHA1 Message Date
Khalil Meftah ef8bfffbd7 fix(rl): enhance intervention handling in actor and learner 2026-04-26 23:09:33 +02:00
Khalil Meftah f887ab3f6a fix(rl): improve action processing for discrete and continuous actions 2026-04-26 22:47:52 +02:00
Khalil Meftah c2556439e5 fix(rl): postprocess action in actor 2026-04-26 18:15:04 +02:00
Khalil Meftah d2a046dfc5 fix(rl): mirror gym_manipulator in actor 2026-04-26 18:11:26 +02:00
Khalil Meftah 613d581f6c remove debug 2026-04-26 18:08:13 +02:00
Khalil Meftah 58b6d844c4 debug 2026-04-26 17:33:15 +02:00
Khalil Meftah 30e1886b64 fix(rl): merge environment and action-processor info in transition processing 2026-04-26 17:12:37 +02:00
Khalil Meftah 9c9064e5be fix(rl): update neutral gripper action 2026-04-26 16:42:53 +02:00
Khalil Meftah 494f469a2b fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100 2026-04-26 16:41:55 +02:00
Khalil Meftah cd105f65cb fix(rl): add time limit processor to environment pipeline 2026-04-26 16:38:20 +02:00
Khalil Meftah 9c2af818ff fix(rl): correctly wire HIL-SERL gripper penalty through processor pipeline 2026-04-26 16:36:21 +02:00
Khalil Meftah 6495bb9706 add processor to main 2026-04-24 17:06:57 +02:00
Steven Palma 580d818aa9 fix(dataset): no default overwrite in lerobot tool recompute stats (#3452) 2026-04-24 15:07:19 +02:00
Steven Palma 587aa82021 fix(imports): realsense import name is platform dependent (#3451) 2026-04-24 12:55:38 +02:00
Chuyao Shen 12b88fce02 not use dataclass (#3414)
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
2026-04-24 11:26:59 +02:00
masato-ka fc6c94c82a fix(sarm): handle BaseModelOutputWithPooling from transformers 5.x in… (#3419)
* fix(sarm): handle BaseModelOutputWithPooling from transformers 5.x in CLIP encoding

In transformers 5.x, CLIPModel.get_image_features() and get_text_features()
return BaseModelOutputWithPooling instead of a plain torch.FloatTensor.
Added isinstance check to extract pooler_output when the return value is not
a tensor, maintaining backward compatibility with transformers 4.x.

Fixes AttributeError: 'BaseModelOutputWithPooling' object has no attribute 'detach'

* Adding assertion check for pooler_output of CLIP. This change is response to below comment.
https://github.com/huggingface/lerobot/pull/3419#discussion_r3112594387

* Adding assertion check for pooler_output of CLIP. This change is response to below comment. Change to simple check and rise
https://github.com/huggingface/lerobot/pull/3419#discussion_r3126953776

---------
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
2026-04-23 16:26:58 +02:00
Steven Palma 1add460678 fix(policy): loss normalization for padded actions in ACT, Diffusion, and MultiTaskDiT (#3442)
* Fix loss normalization for padded actions in ACT, Diffusion, and MultiTaskDiT

When action_is_pad masks out padded timesteps, the subsequent .mean()
still divides by the total element count (including zeroed-out padding),
underestimating the loss. With 60-70% padding this can cut the effective
gradient signal by 2-3x.

Replace mask-then-mean with mask-then-sum / valid-count for all three
affected policies. TDMPC is not affected because it sums over time
before averaging over batch.

Fixes #3353

* linting

Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com>
Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>

* Update src/lerobot/policies/diffusion/modeling_diffusion.py

Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

* Update src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

* Update src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

* apply ACT loss normalization suggestion from review

Divide by num_valid (timesteps * action_dim) instead of just timesteps,
matching the diffusion/multi_task_dit fix. Addresses review from
@whats2000 (https://github.com/huggingface/lerobot/pull/3377#discussion_r3106845791).

* fix(test): update safetensor act

---------

Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
Co-authored-by: Maxime Ellerbach <maxime@ellerbach.net>
Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com>
2026-04-23 15:23:54 +02:00
Qi Jia 4587c2b648 fix xvla docs (#3291)
Co-authored-by: Qi Jia <kaufou@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
2026-04-23 14:50:32 +02:00
whats2000 2236cdb302 fix(smolvla): correct loss normalization for padded actions (#3434)
Apply the same per-scalar-mean fix to SmolVLA that #3377 landed for
ACT / Diffusion / MultiTaskDiT. The pre-patch form applies the
`action_is_pad` mask to zero out padded timesteps, then calls `.mean()`
(or `.mean(dim=(1, 2))`). Because `.mean()` divides by the total number
of elements including the zeroed padding, the loss is diluted by the
padding fraction.

Fixed by normalizing only over valid (non-padded) scalar entries:

    num_valid = ((~actions_is_pad).sum(...) * losses.shape[-1]).clamp_min(1)
    loss = losses.sum(...) / num_valid

`clamp_min(1)` preserves the all-padded-batch edge case (0/1 = 0). Both
reduction paths are updated. Behavior when `action_is_pad` is missing is
unchanged (`losses.mean()`).

Empirical A/B on aloha_sim_transfer_cube_human (chunk_size=40, batch=2,
30 steps, fixed seed, GB200) shows `loss_A / loss_B = 0.9672 (±0.088)` —
same direction and magnitude as PR #3377's `loss_A / loss_C ≈ 0.96` for
ACT. Heavier-padding recipes will see a larger gap.

Refs: #3353 (original report for ACT), #3377 (fix for the other three
policies).
2026-04-23 10:34:11 +02:00
Steven Palma 7c2466979e chore(dependencies): update uv.lock (#3408)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
2026-04-22 16:38:51 +02:00
Pepijn 39b966e20a docs(agents): add AGENT_GUIDE.md for user facing agent (#3430)
* docs(agents): add AGENT_GUIDE.md with SO-101, data, policy, training, eval guidance

Adds an agent-facing companion to AGENTS.md that helps AI agents (Cursor,
Claude, ChatGPT, etc.) guide end-users through LeRobot without needing to
re-read every doc:

- Mandatory "ask the user first" block (goal, hardware, GPU, skill level)
- SO-101 end-to-end cheat-sheet: install -> calibrate -> record -> train -> eval
- Data-collection tips distilled from the folding project (practice before
  you record, quality > speed, start constrained then add diversity)
- Policy decision table with indicative profiling numbers (update ms, peak
  GPU mem) and AdamW-vs-SGD caveats
- Training duration guidance: 5-10 epoch rule, epoch<->step conversion,
  scheduler/checkpoint scaling with --steps, SmolVLA unfreeze tip
- Real-robot eval via lerobot-record --policy.path and sim eval via
  lerobot-eval, including the pre-baked docker/Dockerfile.benchmark.* images

AGENTS.md gets a short pointer to AGENT_GUIDE.md at the top.
CLAUDE.md (symlink to AGENTS.md) inherits the pointer automatically.

Made-with: Cursor

* docs(agents): recommend 2 cameras (front + wrist) as default

Made-with: Cursor

* docs(agents): add Feetech wiring check and broaden visualizer note

Made-with: Cursor

* docs(agents): clarify Feetech LED behavior (steady-on, not flash)

Made-with: Cursor

* docs(agents): expand Feetech troubleshooting (blinking LED, 5V vs 12V variants)

Made-with: Cursor

* docs(agents): tighten Feetech LED wording

Made-with: Cursor
2026-04-22 11:54:19 +02:00
Pepijn ba27aab79c fix(robotwin): pin compatible curobo in benchmark image (#3427)
* fix(robotwin): pin compatible curobo in benchmark image

* fix(robotwin): make curobo smoke check gpu-free
2026-04-21 19:51:44 +02:00
24 changed files with 944 additions and 418 deletions
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This file provides guidance to AI agents when working with code in this repository.
> **User-facing help → [`AGENT_GUIDE.md`](./AGENT_GUIDE.md)** (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands).
## Project Overview
LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing.
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# AGENT_GUIDE.md — LeRobot Helper for AI Agents & Users
This file is a practical, copy-paste-friendly companion for any AI agent (Cursor, Claude, ChatGPT, Codex, etc.) helping a user work with LeRobot. It complements [`AGENTS.md`](./AGENTS.md) (dev/contributor context) with **user-facing guidance**: how to start, what to train, how long, how to record, and how to calibrate an SO-101.
---
## 1. Start here — ask the user first (MANDATORY)
Before suggesting any command, an agent MUST ask the user at least these questions and wait for answers:
1. **What's your goal?** (e.g. "teach my SO-101 to fold a cloth", "train a policy on an existing HF dataset", "contribute a PR", "understand the codebase")
2. **What hardware do you have?**
- Robot: none / SO-100 / SO-101 / Koch / LeKiwi / Reachy / other
- Teleop: leader arm / phone / keyboard / gamepad / none
- Cameras: how many, resolution, fixed or moving?
3. **What machine will you train on?**
- GPU model + VRAM (e.g. "laptop 3060 6 GB", "RTX 4090 24 GB", "A100 80 GB", "CPU only")
- OS: macOS / Linux / Windows
4. **Skill level & time budget?** First time, some ML, experienced? Hours, days, a weekend?
5. **Do you already have a dataset?** Yes (HF repo id?) / no / want to record one
6. **How can I help right now?** (pick one concrete next step)
Only after you have answers, propose a concrete path. If something is ambiguous, ask again rather than guessing. Bias toward **the simplest thing that works** for the user's hardware and goal.
---
## 2. LeRobot in 60 seconds
LeRobot = **datasets + policies + envs + robot control**, unified by a small set of strong abstractions.
- **`LeRobotDataset`** — episode-aware dataset (video or images + actions + state), loadable from the Hub or disk.
- **Policies** (`ACT`, `Diffusion`, `SmolVLA`, `π0`, `π0.5`, `Wall-X`, `X-VLA`, `VQ-BeT`, `TD-MPC`, …) — all inherit `PreTrainedPolicy` and can be pushed/pulled from the Hub.
- **Processors** — small composable transforms between dataset → policy → robot.
- **Envs** (sim) and **Robots** (real) — same action/observation contract so code swaps cleanly.
- **CLI** — `lerobot-record`, `lerobot-train`, `lerobot-eval`, `lerobot-teleoperate`, `lerobot-calibrate`, `lerobot-find-port`, `lerobot-setup-motors`, `lerobot-replay`.
See [`AGENTS.md`](./AGENTS.md) for repo architecture.
---
## 3. Quickstart paths (pick one)
### Path A — "I have an SO-101 and want my first trained policy"
Go to §4 (SO-101 end-to-end), then §5 (data tips), then §6 (pick a policy — likely **ACT**), then §7 (how long), then §8 (eval).
### Path B — "No hardware, I want to train on an existing dataset"
Skip §4. Pick a policy in §6, pick a duration in §7, then run `lerobot-train` per §4.9 with a Hub `--dataset.repo_id` and an `--env.type` for eval. Finish with §8.
### Path C — "I just want to understand the codebase"
Read §2 above, then `AGENTS.md` "Architecture", then open `src/lerobot/policies/act/` and `src/lerobot/datasets/lerobot_dataset.py` as canonical examples.
---
## 4. SO-101 end-to-end cheat-sheet
Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/source/il_robots.mdx`](./docs/source/il_robots.mdx). Minimum commands in order. Confirm arms are assembled + powered before issuing.
**4.1 Install**
```bash
pip install 'lerobot[feetech]' # SO-100/SO-101 motor stack
# pip install 'lerobot[all]' # everything
# pip install 'lerobot[aloha,pusht]' # specific features
# pip install 'lerobot[smolvla]' # add SmolVLA deps
git lfs install && git lfs pull
hf auth login # required to push datasets/policies
```
Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
**4.2 Find USB ports** — run once per arm, unplug when prompted.
```bash
lerobot-find-port
```
macOS: `/dev/tty.usbmodem...`; Linux: `/dev/ttyACM0` (may need `sudo chmod 666 /dev/ttyACM0`).
**4.3 Setup motor IDs & baudrate** (one-time, per arm)
```bash
lerobot-setup-motors --robot.type=so101_follower --robot.port=<FOLLOWER_PORT>
lerobot-setup-motors --teleop.type=so101_leader --teleop.port=<LEADER_PORT>
```
**4.4 Calibrate** — center all joints, press Enter, sweep each joint through its full range. The `id` is the calibration key — reuse it everywhere.
```bash
lerobot-calibrate --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower
lerobot-calibrate --teleop.type=so101_leader --teleop.port=<LEADER_PORT> --teleop.id=my_leader
```
**4.5 Teleoperate** (sanity check, no recording)
```bash
lerobot-teleoperate \
--robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
--teleop.type=so101_leader --teleop.port=<LEADER_PORT> --teleop.id=my_leader \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--display_data=true
```
> **Feetech timeout / comms error on SO-100 / SO-101?** Before touching software, check the **red motor LEDs** on the daisy chain.
>
> - **All steady red, gripper → base chain** → wiring OK.
> - **One or more motors dark / chain stops mid-way** → wiring issue: reseat the 3-pin cables, check the controller-board power supply, and make sure each motor is fully clicked in.
> - **LEDs blinking** → the motor is in an **error state**: usually overload (forcing a joint past its limit) **or wrong power supply voltage**. SO-100 / SO-101 ship in two variants — a **5 V / 7.4 V** build and a **12 V** build — they are NOT interchangeable. Using a 12 V PSU on a 5 V / 7.4 V arm (or vice-versa) will trip this error; confirm your motor variant before powering up.
>
> Most "timeout" errors are physical, not code.
**4.6 Record a dataset** — keys: **→** next, **←** redo, **ESC** finish & upload.
```bash
HF_USER=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
lerobot-record \
--robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
--teleop.type=so101_leader --teleop.port=<LEADER_PORT> --teleop.id=my_leader \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--dataset.repo_id=${HF_USER}/my_task \
--dataset.single_task="<describe the task in one sentence>" \
--dataset.num_episodes=50 \
--dataset.episode_time_s=30 \
--dataset.reset_time_s=10 \
--display_data=true
```
**4.7 Visualize****always** do this before training. Look for missing frames, camera blur, unreachable targets, inconsistent object positions.
After upload: https://huggingface.co/spaces/lerobot/visualize_dataset → paste `${HF_USER}/my_task`. Works for **any LeRobot-formatted Hub dataset** — use it to scout other datasets, inspect episode quality, or debug your own data before retraining.
**4.8 Replay an episode** (sanity check)
```bash
lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
--dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
```
**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/my_task \
--policy.type=act \
--policy.device=cuda \
--output_dir=outputs/train/act_my_task \
--job_name=act_my_task \
--batch_size=8 \
--wandb.enable=true \
--policy.repo_id=${HF_USER}/act_my_task
```
**4.10 Evaluate on the real robot** — compare success rate to a teleoperated baseline.
```bash
lerobot-record \
--robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--dataset.repo_id=${HF_USER}/eval_my_task \
--dataset.single_task="<same task description as training>" \
--dataset.num_episodes=10 \
--policy.path=${HF_USER}/act_my_task
```
---
## 5. Data collection tips (beginner → reliable policy)
Good data beats clever models. Adopt these defaults and deviate only with evidence.
### 5.1 Setup & ergonomics
- **Fix the rig and cameras** before touching the software. If the rig vibrates or the operator gets frustrated, fix that first — more bad data won't help.
- **Lighting matters more than resolution.** Diffuse, consistent light. Avoid moving shadows.
- **"Can you do the task from the camera view alone?"** If no, your cameras are wrong. Fix before recording.
- Enable **action interpolation** for rollouts when available for smoother trajectories.
### 5.2 Practice before you record
- Do 510 demos without recording. Build a deliberate, repeatable strategy.
- Hesitant or inconsistent demos teach the model hesitation.
### 5.3 Quality over speed
Deliberate, high-quality execution beats fast sloppy runs. Optimize for speed only **after** strategy is dialed in — never trade quality for it.
### 5.4 Consistency within and across episodes
Same grasp, approach vector, and timing. Coherent strategies are much easier to learn than wildly varying movements.
### 5.5 Start small, then extend (the golden rule)
- **First 50 episodes = constrained version** of the task: one object, fixed position, fixed camera setup, one operator.
- Train a quick ACT model. See what fails.
- **Then add diversity** along one axis at a time: more positions → more lighting → more objects → more operators.
- Don't try to collect the "perfect dataset" on day one. Iterate.
### 5.6 Policy choice for beginners
- **Laptop / first time / want results fast → ACT.** Works surprisingly well, trains fast even on a laptop GPU.
- **Bigger GPU / language-conditioned / multi-task → SmolVLA.** Unfreezing the vision encoder (see §7) is a big win here.
- Defer π0 / π0.5 / Wall-X / X-VLA until you have a proven ACT baseline and a 20+ GB GPU.
### 5.7 Recommended defaults for your first task
| Setting | Value |
| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| Episodes | **50** to start, scale to 100300 after first training |
| Episode length | 2045 s (shorter is fine for grasp/place) |
| Reset time | 10 s |
| FPS | 30 |
| Cameras | **2 cameras recommended**: 1 fixed front + 1 wrist. Multi-view often outperforms single-view. A single fixed camera also works to keep things simple. |
| Task description | Short, specific, action-phrased sentence |
### 5.8 Troubleshooting signal
- Policy fails at one specific stage → record 1020 more episodes **targeting that stage**.
- Policy flaps / oscillates → likely inconsistent demos, or need more training; re-record worst episodes (use **←** to redo).
- Policy ignores the object → camera framing or lighting issue, not a model issue.
See also: [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset).
---
## 6. Which policy should I train?
Match the policy to the user's **GPU memory** and **time budget**. Numbers below come from an internal profiling run (one training update per policy). They are **indicative only** — see caveats.
### 6.1 Profiling snapshot (indicative)
All policies typically train for **510 epochs** (see §7).
| Policy | Batch | Update (ms) | Peak GPU mem (GB) | Best for |
| ----------- | ----: | ----------: | ----------------: | ------------------------------------------------------------------------------------------------ |
| `act` | 4 | **83.9** | **0.94** | First-time users, laptops, single-task. Fast and reliable. |
| `diffusion` | 4 | 168.6 | 4.94 | Multi-modal action distributions; needs mid-range GPU. |
| `smolvla` | 1 | 357.8 | 3.93 | Language-conditioned, multi-task, small VLA. **Unfreeze vision encoder for big gains** (see §7). |
| `xvla` | 1 | 731.6 | 15.52 | Large VLA, multi-task. |
| `wall_x` | 1 | 716.5 | 15.95 | Large VLA with world-model objective. |
| `pi0` | 1 | 940.3 | 15.50 | Strong large VLA baseline (Physical Intelligence). |
| `pi05` | 1 | 1055.8 | 16.35 | Newer π policy; similar footprint to `pi0`. |
**Critical caveats:**
- **Optimizer:** measured with **SGD**. LeRobot's default is **AdamW**, which keeps extra optimizer state → **peak memory will be noticeably higher** with the default, especially for `pi0`, `pi05`, `wall_x`, `xvla`.
- **Batch size:** the large policies were profiled at batch 1. In practice use a **larger batch** for stable training (see §7.4). Memory scales roughly linearly with batch.
### 6.2 Decision rules
- **< 8 GB VRAM (laptop, 3060, M-series Mac):** → `act`. Maybe `diffusion` if you have ~68 GB free.
- **1216 GB VRAM (4070/4080, A4000):** → `smolvla` with defaults, or `act`/`diffusion` with larger batch. `pi0`/`pi05`/`wall_x`/`xvla` feasible only with small batch + gradient accumulation.
- **24+ GB VRAM (3090/4090/A5000):** → any policy. Prefer `smolvla` (unfrozen) for multi-task; `act` for single-task grasp-and-place (still often the best ROI). Could experiment with `pi0` or `pi05` or `xvla`
- **80 GB (A100/H100):** → any, with healthy batch. `pi05`, `xvla`, `wall_x` become comfortable.
- **CPU only:** → don't train here. Use Google Colab (see [`docs/source/notebooks.mdx`](./docs/source/notebooks.mdx)) or a rented GPU.
---
## 7. How long should I train?
Robotics imitation learning usually converges in a **few epochs over the dataset**, not hundreds of thousands of raw steps. Think **epochs first**, then translate to steps.
### 7.1 Rule of thumb
- **Typical total: 510 epochs.** Start at 5, eval, then decide if more helps.
- Very small datasets (< 30 episodes) may want slightly more epochs — but first, **collect more data**.
- VLAs with a pretrained vision backbone typically need **fewer** epochs than training from scratch.
### 7.2 Steps ↔ epochs conversion
```
total_frames = sum of frames over all episodes # e.g. 50 eps × 30 fps × 30 s ≈ 45,000
steps_per_epoch = ceil(total_frames / batch_size)
total_steps = epochs × steps_per_epoch
```
Examples for `--batch_size=8`:
| Dataset size | Frames | Steps / epoch | 5 epochs | 10 epochs |
| ----------------------- | ------: | ------------: | -------: | --------: |
| 50 eps × 30 s @ 30 fps | 45,000 | ~5,625 | 28k | 56k |
| 100 eps × 30 s @ 30 fps | 90,000 | ~11,250 | 56k | 113k |
| 300 eps × 30 s @ 30 fps | 270,000 | ~33,750 | 169k | 338k |
Pass the resulting total with `--steps=<N>`; eval at intermediate checkpoints (`outputs/train/.../checkpoints/`).
### 7.3 Per-policy starting points (single-task, ~50 episodes)
| Policy | Batch | Steps (first run) | Notes |
| -------------- | ----: | ----------------: | ----------------------------------------------------------------- |
| `act` | 816 | 30k80k | Usually converges under 50k for single-task. |
| `diffusion` | 816 | 80k150k | Benefits from longer training than ACT. |
| `smolvla` | 48 | 30k80k | Pretrained VLM → converges fast. |
| `pi0` / `pi05` | 14 | 30k80k | Memory-bound; use gradient accumulation for effective batch ≥ 16! |
### 7.4 Batch size guidance
- **Bigger batch is preferable** for stable gradients on teleop data.
- If GPU memory is the bottleneck, use **gradient accumulation** to raise _effective_ batch without raising peak memory.
- Scale **learning rate** gently with batch; most LeRobot defaults work fine for a 24× batch change.
### 7.5 Scale LR schedule & checkpoints with `--steps`
LeRobot's default schedulers (e.g. SmolVLA's cosine decay) use `scheduler_decay_steps=30_000`, which is sized for long training runs. When you shorten training (e.g. 5k10k steps on a small dataset), **scale the scheduler down to match** — otherwise the LR stays near the peak and never decays. Same for checkpoint frequency.
```bash
lerobot-train ... \
--steps=5000 \
--policy.scheduler_decay_steps=5000 \
--save_freq=5000
```
Rule of thumb: set `scheduler_decay_steps ≈ steps`, and `save_freq` to whatever granularity you want for eval (e.g. every 1k5k steps). Match `scheduler_warmup_steps` proportionally if your run is very short.
### 7.6 SmolVLA: unfreeze the vision encoder for real gains
SmolVLA ships with `freeze_vision_encoder=True`. Unfreezing usually **improves performance substantially** on specialized tasks, at the cost of more VRAM and slower steps. Enable with:
```bash
lerobot-train ... --policy.type=smolvla \
--policy.freeze_vision_encoder=false \
--policy.train_expert_only=false
```
### 7.7 Signals to stop / keep going
- Train loss plateaus → stop, save a Hub checkpoint.
- Train loss still dropping and you're under 10 epochs → keep going.
---
## 8. Evaluation & benchmarks
Two flavors of evaluation:
### 8.1 Real-robot eval (SO-101, etc.)
Reuse `lerobot-record` with `--policy.path` to run the trained policy on-robot and save the run as an eval dataset. Convention: prefix the dataset with `eval_`.
```bash
lerobot-record \
--robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--dataset.repo_id=${HF_USER}/eval_my_task \
--dataset.single_task="<same task description used during training>" \
--dataset.num_episodes=10 \
--policy.path=${HF_USER}/act_my_task
```
Report success rate across episodes. Compare to a teleoperated baseline and to an earlier checkpoint to catch regressions.
### 8.2 Sim-benchmark eval
For policies trained on sim datasets (PushT, Aloha, LIBERO, MetaWorld, RoboCasa, …) use `lerobot-eval` against the matching `env.type`:
```bash
lerobot-eval \
--policy.path=${HF_USER}/diffusion_pusht \
--env.type=pusht \
--eval.n_episodes=50 \
--eval.batch_size=10 \
--policy.device=cuda
```
- Use `--policy.path=outputs/train/.../checkpoints/<step>/pretrained_model` for local checkpoints.
- `--eval.n_episodes` should be ≥ 50 for a stable success-rate estimate.
- Available envs live in `src/lerobot/envs/`. See [`docs/source/libero.mdx`](./docs/source/libero.mdx), [`metaworld.mdx`](./docs/source/metaworld.mdx), [`robocasa.mdx`](./docs/source/robocasa.mdx), [`vlabench.mdx`](./docs/source/vlabench.mdx) for specific benchmarks.
- To add a new benchmark, see [`docs/source/adding_benchmarks.mdx`](./docs/source/adding_benchmarks.mdx) and [`envhub.mdx`](./docs/source/envhub.mdx).
### 8.2b Dockerfiles for benchmark eval
Benchmark envs have native dependencies that are painful to install locally. The repo ships **pre-baked Dockerfiles** for each supported benchmark — use these to run `lerobot-eval` in a reproducible environment:
| Benchmark | Dockerfile |
| ----------- | -------------------------------------------------------------------------------------- |
| LIBERO | [`docker/Dockerfile.benchmark.libero`](./docker/Dockerfile.benchmark.libero) |
| LIBERO+ | [`docker/Dockerfile.benchmark.libero_plus`](./docker/Dockerfile.benchmark.libero_plus) |
| MetaWorld | [`docker/Dockerfile.benchmark.metaworld`](./docker/Dockerfile.benchmark.metaworld) |
| RoboCasa | [`docker/Dockerfile.benchmark.robocasa`](./docker/Dockerfile.benchmark.robocasa) |
| RoboCerebra | [`docker/Dockerfile.benchmark.robocerebra`](./docker/Dockerfile.benchmark.robocerebra) |
| RoboMME | [`docker/Dockerfile.benchmark.robomme`](./docker/Dockerfile.benchmark.robomme) |
| RoboTwin | [`docker/Dockerfile.benchmark.robotwin`](./docker/Dockerfile.benchmark.robotwin) |
| VLABench | [`docker/Dockerfile.benchmark.vlabench`](./docker/Dockerfile.benchmark.vlabench) |
Build and run (adapt to your benchmark):
```bash
docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-bench-robomme .
docker run --gpus all --rm -it \
-v $HOME/.cache/huggingface:/root/.cache/huggingface \
lerobot-bench-robomme \
lerobot-eval --policy.path=<your_policy> --env.type=<env> --eval.n_episodes=50
```
See [`docker/README.md`](./docker/README.md) for base-image details.
### 8.3 Target success rates
Single-task grasp-and-place with 50 clean episodes: ACT should reach **> 70% success** on the training configuration. Less → data problem (see §5), not model problem. Expect a drop when generalizing to new positions — scale episodes or diversity to recover.
---
## 9. Further reading & resources
- **Getting started:** [`installation.mdx`](./docs/source/installation.mdx) · [`il_robots.mdx`](./docs/source/il_robots.mdx) · [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets)
- **Per-policy docs:** browse [`docs/source/*.mdx`](./docs/source/) (policies, hardware, benchmarks, advanced training).
- **Community:** [Discord](https://discord.com/invite/s3KuuzsPFb) · [Hub `LeRobot` tag](https://huggingface.co/datasets?other=LeRobot) · [Dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset)
> Keep this file current. If you learn a rule that would prevent a class of user mistakes, add it here and in [`AGENTS.md`](./AGENTS.md).
+21 -5
View File
@@ -56,11 +56,11 @@ RUN uv pip install --no-cache --no-build-isolation \
"git+https://github.com/facebookresearch/pytorch3d.git@stable"
# CuRobo — NVlabs motion generator; TORCH_CUDA_ARCH_LIST must be set or the
# build aborts on an empty arch list. Pinned SHA for reproducibility.
ARG CUROBO_SHA=ca941586c33b8482ed9c0e74d60f23efd64b516a
# build aborts on an empty arch list. RoboTwin's own installer pins v0.7.8,
# which still exposes the v1 API (`curobo.types.math`) that RoboTwin imports.
ARG CUROBO_REF=v0.7.8
RUN cd ${ROBOTWIN_ROOT}/envs \
&& git clone https://github.com/NVlabs/curobo.git \
&& git -C curobo checkout ${CUROBO_SHA} \
&& git clone --branch ${CUROBO_REF} --depth 1 https://github.com/NVlabs/curobo.git \
&& cd curobo \
&& TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" \
uv pip install -e . --no-build-isolation --no-cache
@@ -111,7 +111,23 @@ EOF
WORKDIR ${ROBOTWIN_ROOT}
RUN python script/update_embodiment_config_path.py
ENV PYTHONPATH="${ROBOTWIN_ROOT}:${PYTHONPATH}"
ENV PYTHONPATH="${ROBOTWIN_ROOT}"
# Fail the image build early if the CuRobo package layout regresses. Importing
# RoboTwin's planner here is too eager because CuRobo constructs CUDA-backed
# defaults at import time, while Docker builds don't have access to an NVIDIA
# driver.
RUN python - <<'EOF'
from pathlib import Path
from curobo.types.math import Pose
planner_src = (Path("/opt/robotwin/envs/robot/planner.py")).read_text()
assert "from curobo.types.math import Pose as CuroboPose" in planner_src
print("CuRobo import OK:", Pose.__name__)
print("RoboTwin planner import references curobo.types.math")
EOF
# Return to the lerobot source directory (set by base image) before overlaying.
WORKDIR /lerobot
+4 -4
View File
@@ -220,7 +220,7 @@ REAL_DIM = 12
# Postprocessing: Trim 20D predictions to 12D for deployment
```
See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
See the [action_hub.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py) implementation for details.
#### Auto Action Mode (Recommended)
@@ -519,9 +519,9 @@ If you use X-VLA in your research, please cite:
- [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
- [LeRobot Documentation](https://github.com/huggingface/lerobot)
- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
- [Action Registry Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py)
- [Processor Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/processor_xvla.py)
- [Model Configuration](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/configuration_xvla.py)
## Contributing
@@ -17,6 +17,7 @@ Provides the RealSenseCamera class for capturing frames from Intel RealSense cam
"""
import logging
import sys
import time
from threading import Event, Lock, Thread
from typing import TYPE_CHECKING, Any
@@ -41,6 +42,7 @@ from ..utils import get_cv2_rotation
from .configuration_realsense import RealSenseCameraConfig
logger = logging.getLogger(__name__)
pkg_name = "pyrealsense2-macosx" if sys.platform == "darwin" else "pyrealsense2"
class RealSenseCamera(Camera):
@@ -114,7 +116,7 @@ class RealSenseCamera(Camera):
Args:
config: The configuration settings for the camera.
"""
require_package("pyrealsense2", extra="intelrealsense")
require_package(pkg_name, extra="intelrealsense", import_name="pyrealsense2")
super().__init__(config)
self.config = config
+4 -3
View File
@@ -142,9 +142,10 @@ class ACTPolicy(PreTrainedPolicy):
actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
l1_loss = (
F.l1_loss(batch[ACTION], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
).mean()
abs_err = F.l1_loss(batch[ACTION], actions_hat, reduction="none")
valid_mask = ~batch["action_is_pad"].unsqueeze(-1)
num_valid = valid_mask.sum() * abs_err.shape[-1]
l1_loss = (abs_err * valid_mask).sum() / num_valid.clamp_min(1)
loss_dict = {"l1_loss": l1_loss.item()}
if self.config.use_vae:
@@ -380,7 +380,9 @@ class DiffusionModel(nn.Module):
f"{self.config.do_mask_loss_for_padding=}."
)
in_episode_bound = ~batch["action_is_pad"]
loss = loss * in_episode_bound.unsqueeze(-1)
mask = in_episode_bound.unsqueeze(-1)
num_valid = mask.sum() * loss.shape[-1]
return (loss * mask).sum() / num_valid.clamp_min(1)
return loss.mean()
+5 -9
View File
@@ -13,7 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING
@@ -174,17 +173,14 @@ N_COLOR_CHANNELS = 3
# config
@dataclass
class GR00TN15Config(PretrainedConfig):
model_type = "gr00t_n1_5"
backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
action_dim: int = field(init=False, metadata={"help": "Action dimension."})
compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
backbone_cfg: dict
action_head_cfg: dict
action_horizon: int
action_dim: int
compute_dtype: str = "float32"
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -688,8 +688,9 @@ class DiffusionObjective(nn.Module):
loss = F.mse_loss(predicted, target, reduction="none")
if self.do_mask_loss_for_padding and "action_is_pad" in batch:
valid_actions = ~batch["action_is_pad"]
loss = loss * valid_actions.unsqueeze(-1)
mask = ~batch["action_is_pad"].unsqueeze(-1)
num_valid = mask.sum() * loss.shape[-1]
return (loss * mask).sum() / num_valid.clamp_min(1)
return loss.mean()
@@ -752,8 +753,9 @@ class FlowMatchingObjective(nn.Module):
loss = F.mse_loss(predicted_velocity, target_velocity, reduction="none")
if self.do_mask_loss_for_padding and "action_is_pad" in batch:
valid_mask = ~batch["action_is_pad"]
loss = loss * valid_mask.unsqueeze(-1)
mask = ~batch["action_is_pad"].unsqueeze(-1)
num_valid = mask.sum() * loss.shape[-1]
return (loss * mask).sum() / num_valid.clamp_min(1)
return loss.mean()
+14 -2
View File
@@ -455,7 +455,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
inputs = {k: v.to(self.device) for k, v in inputs.items()}
# Get image embeddings
embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
# transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
output = self.clip_model.get_image_features(**inputs)
if not isinstance(output, torch.Tensor):
output = output.pooler_output
if output is None:
raise ValueError("pooler_output should not be None for CLIP models.")
embeddings = output.detach().cpu()
# Handle single frame case
if embeddings.dim() == 1:
@@ -482,7 +488,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
inputs = self.clip_processor.tokenizer([text], return_tensors="pt", padding=True, truncation=True)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
# transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
output = self.clip_model.get_text_features(**inputs)
if not isinstance(output, torch.Tensor):
output = output.pooler_output
if output is None:
raise ValueError("pooler_output should not be None for CLIP models.")
text_embedding = output.detach().cpu()
text_embedding = text_embedding.expand(batch_size, -1)
return text_embedding
@@ -394,13 +394,21 @@ class SmolVLAPolicy(PreTrainedPolicy):
loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
if reduction == "none":
# Return per-sample losses (B,) by averaging over time and action dims
per_sample_loss = losses.mean(dim=(1, 2))
# Return per-sample losses (B,) by averaging over valid (time, action) entries
if actions_is_pad is None:
per_sample_loss = losses.mean(dim=(1, 2))
else:
num_valid = ((~actions_is_pad).sum(dim=1) * losses.shape[-1]).clamp_min(1)
per_sample_loss = losses.sum(dim=(1, 2)) / num_valid
loss_dict["loss"] = per_sample_loss.mean().item()
return per_sample_loss, loss_dict
else:
# Default: return scalar mean loss
loss = losses.mean()
# Default: return scalar mean loss over valid (time, action) entries
if actions_is_pad is None:
loss = losses.mean()
else:
num_valid = ((~actions_is_pad).sum() * losses.shape[-1]).clamp_min(1)
loss = losses.sum() / num_valid
loss_dict["loss"] = loss.item()
return loss, loss_dict
+25 -8
View File
@@ -321,6 +321,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
This step normalizes the `transition` object by:
1. Copying `teleop_action` from `info` to `complementary_data`.
2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
3. Copying `discrete_penalty` from `info` to `complementary_data`.
"""
def __call__(self, transition: EnvTransition) -> EnvTransition:
@@ -330,6 +331,9 @@ class GymHILAdapterProcessorStep(ProcessorStep):
if TELEOP_ACTION_KEY in info:
complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
if DISCRETE_PENALTY_KEY in info:
complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
if "is_intervention" in info:
info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
@@ -348,18 +352,24 @@ class GymHILAdapterProcessorStep(ProcessorStep):
@ProcessorStepRegistry.register("gripper_penalty_processor")
class GripperPenaltyProcessorStep(ProcessorStep):
"""
Applies a penalty for inefficient gripper usage.
Applies a small per-transition cost on the discrete gripper action.
This step penalizes actions that attempt to close an already closed gripper or
open an already open one, based on position thresholds.
Fires only when the commanded action would actually transition the gripper
from one extreme to the other (close-while-open or open-while-closed).
This discourages gripper oscillation while leaving "stay" and saturating-further
commands unpenalized.
Attributes:
penalty: The negative reward value to apply.
max_gripper_pos: The maximum position value for the gripper, used for normalization.
open_threshold: Normalized state below which the gripper is considered "open".
closed_threshold: Normalized state above which the gripper is considered "closed".
"""
penalty: float = -0.01
penalty: float = -0.02
max_gripper_pos: float = 30.0
open_threshold: float = 0.1
closed_threshold: float = 0.9
def __call__(self, transition: EnvTransition) -> EnvTransition:
"""
@@ -391,9 +401,13 @@ class GripperPenaltyProcessorStep(ProcessorStep):
gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
# Calculate penalty boolean as in original
gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
)
# - currently open AND target is closed -> close transition
# - currently closed AND target is open -> open transition
is_open = gripper_state_normalized < self.open_threshold
is_closed = gripper_state_normalized > self.closed_threshold
cmd_close = gripper_action_normalized > self.closed_threshold
cmd_open = gripper_action_normalized < self.open_threshold
gripper_penalty_bool = (is_open and cmd_close) or (is_closed and cmd_open)
gripper_penalty = self.penalty * int(gripper_penalty_bool)
@@ -409,11 +423,14 @@ class GripperPenaltyProcessorStep(ProcessorStep):
Returns the configuration of the step for serialization.
Returns:
A dictionary containing the penalty value and max gripper position.
A dictionary containing the penalty value, max gripper position,
and the open/closed thresholds.
"""
return {
"penalty": self.penalty,
"max_gripper_pos": self.max_gripper_pos,
"open_threshold": self.open_threshold,
"closed_threshold": self.closed_threshold,
}
def reset(self) -> None:
@@ -134,6 +134,15 @@ class _NormalizationMixin:
if self.dtype is None:
self.dtype = torch.float32
self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
self._reshape_visual_stats()
def _reshape_visual_stats(self) -> None:
"""Reshape visual stats from ``[C]`` to ``[C, 1, 1]`` for image broadcasting."""
for key, feature in self.features.items():
if feature.type == FeatureType.VISUAL and key in self._tensor_stats:
for stat_name, stat_tensor in self._tensor_stats[key].items():
if isinstance(stat_tensor, Tensor) and stat_tensor.ndim == 1:
self._tensor_stats[key][stat_name] = stat_tensor.reshape(-1, 1, 1)
def to(
self, device: torch.device | str | None = None, dtype: torch.dtype | None = None
@@ -152,6 +161,7 @@ class _NormalizationMixin:
if dtype is not None:
self.dtype = dtype
self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
self._reshape_visual_stats()
return self
def state_dict(self) -> dict[str, Tensor]:
@@ -201,6 +211,7 @@ class _NormalizationMixin:
# Don't load from state_dict, keep the explicitly provided stats
# But ensure _tensor_stats is properly initialized
self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype) # type: ignore[assignment]
self._reshape_visual_stats()
return
# Normal behavior: load stats from state_dict
@@ -211,6 +222,7 @@ class _NormalizationMixin:
self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
dtype=torch.float32, device=self.device
)
self._reshape_visual_stats()
# Reconstruct the original stats dict from tensor stats for compatibility with to() method
# and other functions that rely on self.stats
+29 -19
View File
@@ -60,7 +60,7 @@ from torch.multiprocessing import Event, Queue
from lerobot.cameras import opencv # noqa: F401
from lerobot.configs import parser
from lerobot.configs.train import TrainRLServerPipelineConfig
from lerobot.policies import make_policy
from lerobot.policies import make_policy, make_pre_post_processors
from lerobot.policies.sac.modeling_sac import SACPolicy
from lerobot.robots import so_follower # noqa: F401
from lerobot.teleoperators import gamepad, so_leader # noqa: F401
@@ -89,9 +89,9 @@ from lerobot.utils.utils import (
)
from .gym_manipulator import (
create_transition,
make_processors,
make_robot_env,
reset_and_build_transition,
step_env_and_process_transition,
)
from .process import ProcessSignalHandler
@@ -261,13 +261,12 @@ def act_with_policy(
policy = policy.eval()
assert isinstance(policy, nn.Module)
obs, info = online_env.reset()
env_processor.reset()
action_processor.reset()
preprocessor, postprocessor = make_pre_post_processors(
policy_cfg=cfg.policy,
dataset_stats=cfg.policy.dataset_stats,
)
# Process initial observation
transition = create_transition(observation=obs, info=info)
transition = env_processor(transition)
transition = reset_and_build_transition(online_env, env_processor, action_processor)
# NOTE: For the moment we will solely handle the case of a single environment
sum_reward_episode = 0
@@ -291,8 +290,21 @@ def act_with_policy(
# Time policy inference and check if it meets FPS requirement
with policy_timer:
# Extract observation from transition for policy
action = policy.select_action(batch=observation)
normalized_observation = preprocessor.process_observation(observation)
action = policy.select_action(batch=normalized_observation)
# Unnormalize only the continuous part. When `num_discrete_actions` is set,
# `select_action` concatenates an argmax index in env space at the last dim;
# action stats cover the continuous dims only, so feeding the full vector to
# the unnormalizer would shape-mismatch and would also corrupt the discrete
# index by treating it as a normalized value.
if cfg.policy.num_discrete_actions is not None:
continuous_action = postprocessor.process_action(action[..., :-1])
discrete_action = action[..., -1:].to(
device=continuous_action.device, dtype=continuous_action.dtype
)
action = torch.cat([continuous_action, discrete_action], dim=-1)
else:
action = postprocessor.process_action(action)
policy_fps = policy_timer.fps_last
log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
@@ -326,7 +338,8 @@ def act_with_policy(
# Check for intervention from transition info
intervention_info = new_transition[TransitionKey.INFO]
if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
is_intervention = bool(intervention_info.get(TeleopEvents.IS_INTERVENTION, False))
if is_intervention:
episode_intervention = True
episode_intervention_steps += 1
@@ -334,6 +347,10 @@ def act_with_policy(
"discrete_penalty": torch.tensor(
[new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
),
# Forward the intervention flag so the learner can route this transition
# into the offline replay buffer (see `process_transitions` in learner.py).
# Use the plain string key so the payload survives torch.load(weights_only=True).
TeleopEvents.IS_INTERVENTION.value: is_intervention,
}
# Create transition for learner (convert to old format)
list_transition_to_send_to_learner.append(
@@ -390,14 +407,7 @@ def act_with_policy(
episode_intervention_steps = 0
episode_total_steps = 0
# Reset environment and processors
obs, info = online_env.reset()
env_processor.reset()
action_processor.reset()
# Process initial observation
transition = create_transition(observation=obs, info=info)
transition = env_processor(transition)
transition = reset_and_build_transition(online_env, env_processor, action_processor)
if cfg.env.fps is not None:
dt_time = time.perf_counter() - start_time
+46 -21
View File
@@ -383,10 +383,21 @@ def make_processors(
GymHILAdapterProcessorStep(),
Numpy2TorchActionProcessorStep(),
VanillaObservationProcessorStep(),
AddBatchDimensionProcessorStep(),
DeviceProcessorStep(device=device),
]
# Add time limit processor if reset config exists
if cfg.processor.reset is not None:
env_pipeline_steps.append(
TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps))
)
env_pipeline_steps.extend(
[
AddBatchDimensionProcessorStep(),
DeviceProcessorStep(device=device),
]
)
return DataProcessorPipeline(
steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition
), DataProcessorPipeline(
@@ -551,8 +562,19 @@ def step_env_and_process_transition(
terminated = terminated or processed_action_transition[TransitionKey.DONE]
truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED]
complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy()
if hasattr(env, "get_raw_joint_positions"):
raw_joint_positions = env.get_raw_joint_positions()
if raw_joint_positions is not None:
complementary_data["raw_joint_positions"] = raw_joint_positions
# Merge env and action-processor info: env wins for str keys, action-processor
# wins for `TeleopEvents` enum keys
action_info = processed_action_transition[TransitionKey.INFO]
new_info = info.copy()
new_info.update(processed_action_transition[TransitionKey.INFO])
for key, value in action_info.items():
if isinstance(key, TeleopEvents):
new_info[key] = value
new_transition = create_transition(
observation=obs,
@@ -568,6 +590,24 @@ def step_env_and_process_transition(
return new_transition
def reset_and_build_transition(
env: gym.Env,
env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
action_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
) -> EnvTransition:
"""Reset env + processors and return the first env-processed transition."""
obs, info = env.reset()
env_processor.reset()
action_processor.reset()
complementary_data: dict[str, Any] = {}
if hasattr(env, "get_raw_joint_positions"):
raw_joint_positions = env.get_raw_joint_positions()
if raw_joint_positions is not None:
complementary_data["raw_joint_positions"] = raw_joint_positions
transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
return env_processor(data=transition)
def control_loop(
env: gym.Env,
env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
@@ -593,17 +633,7 @@ def control_loop(
print("- When not intervening, robot will stay still")
print("- Press Ctrl+C to exit")
# Reset environment and processors
obs, info = env.reset()
complementary_data = (
{"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {}
)
env_processor.reset()
action_processor.reset()
# Process initial observation
transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
transition = env_processor(data=transition)
transition = reset_and_build_transition(env, env_processor, action_processor)
# Determine if gripper is used
use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True
@@ -665,7 +695,7 @@ def control_loop(
# Create a neutral action (no movement)
neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
if use_gripper:
neutral_action = torch.cat([neutral_action, torch.tensor([0.0])]) # Gripper stay
neutral_action = torch.cat([neutral_action, torch.tensor([1.0])]) # Gripper stay
# Use the new step function
transition = step_env_and_process_transition(
@@ -723,12 +753,7 @@ def control_loop(
dataset.save_episode()
# Reset for new episode
obs, info = env.reset()
env_processor.reset()
action_processor.reset()
transition = create_transition(observation=obs, info=info)
transition = env_processor(transition)
transition = reset_and_build_transition(env, env_processor, action_processor)
# Maintain fps timing
precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))
+11 -6
View File
@@ -70,7 +70,7 @@ from lerobot.common.wandb_utils import WandBLogger
from lerobot.configs import parser
from lerobot.configs.train import TrainRLServerPipelineConfig
from lerobot.datasets import LeRobotDataset, make_dataset
from lerobot.policies import make_policy
from lerobot.policies import make_policy, make_pre_post_processors
from lerobot.policies.sac.modeling_sac import SACPolicy
from lerobot.robots import so_follower # noqa: F401
from lerobot.teleoperators import gamepad, so_leader # noqa: F401
@@ -317,6 +317,11 @@ def add_actor_information_and_train(
policy.train()
preprocessor, _postprocessor = make_pre_post_processors(
policy_cfg=cfg.policy,
dataset_stats=cfg.policy.dataset_stats,
)
push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
last_time_policy_pushed = time.time()
@@ -405,8 +410,8 @@ def add_actor_information_and_train(
actions = batch[ACTION]
rewards = batch["reward"]
observations = batch["state"]
next_observations = batch["next_state"]
observations = preprocessor.process_observation(batch["state"])
next_observations = preprocessor.process_observation(batch["next_state"])
done = batch["done"]
check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
@@ -463,8 +468,8 @@ def add_actor_information_and_train(
actions = batch[ACTION]
rewards = batch["reward"]
observations = batch["state"]
next_observations = batch["next_state"]
observations = preprocessor.process_observation(batch["state"])
next_observations = preprocessor.process_observation(batch["next_state"])
done = batch["done"]
check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
@@ -1163,7 +1168,7 @@ def process_transitions(
# Add to offline buffer if it's an intervention
if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
TeleopEvents.IS_INTERVENTION
TeleopEvents.IS_INTERVENTION.value
):
offline_replay_buffer.add(**transition)
@@ -353,7 +353,8 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
speed_factor: A scaling factor to convert the normalized velocity command to a position change.
clip_min: The minimum allowed gripper joint position.
clip_max: The maximum allowed gripper joint position.
discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay).
discrete_gripper: If True, interpret the input as a discrete class index
{0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
"""
speed_factor: float = 20.0
@@ -377,10 +378,10 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
raise ValueError("Joints observation is require for computing robot kinematics")
if self.discrete_gripper:
# Discrete gripper actions are in [0, 1, 2]
# 0: open, 1: close, 2: stay
# We need to shift them to [-1, 0, 1] and then scale them to clip_max
gripper_vel = (gripper_vel - 1) * self.clip_max
# Map discrete command {0=close, 1=stay, 2=open} -> signed velocity.
# Negation accounts for SO100 sign (joint position increases on close).
# 0 -> +clip_max (close), 1 -> 0 (stay), 2 -> -clip_max (open)
gripper_vel = -(gripper_vel - 1) * self.clip_max
# Compute desired gripper position
delta = gripper_vel * float(self.speed_factor)
+67 -7
View File
@@ -150,11 +150,24 @@ Show dataset information without feature details:
--operation.type info \
--operation.show_features false
Recompute dataset statistics:
Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default):
lerobot-edit-dataset \
--repo_id lerobot/pusht \
--operation.type recompute_stats
Recompute stats and save to a specific new repo_id:
lerobot-edit-dataset \
--repo_id lerobot/pusht \
--new_repo_id lerobot/pusht_new_stats \
--operation.type recompute_stats
Recompute stats in-place (overwrites original dataset stats):
lerobot-edit-dataset \
--repo_id lerobot/pusht \
--new_repo_id lerobot/pusht \
--operation.type recompute_stats \
--operation.overwrite true
Recompute stats for relative actions and push to hub:
lerobot-edit-dataset \
--repo_id lerobot/pusht \
@@ -256,6 +269,7 @@ class RecomputeStatsConfig(OperationConfig):
relative_exclude_joints: list[str] | None = None
chunk_size: int = 50
num_workers: int = 0
overwrite: bool = False
@OperationConfig.register_subclass("info")
@@ -280,16 +294,30 @@ class EditDatasetConfig:
push_to_hub: bool = False
def _resolve_io_paths(
repo_id: str,
new_repo_id: str | None,
root: Path | str | None,
new_root: Path | str | None,
default_new_repo_id: str | None = None,
) -> tuple[str, Path, Path]:
"""Resolve input/output paths and repo_id for dataset operations.
Returns (output_repo_id, input_path, output_path) with resolved (symlink-safe) paths.
"""
input_path = (Path(root) if root else HF_LEROBOT_HOME / repo_id).resolve()
output_repo_id = new_repo_id or default_new_repo_id or repo_id
output_path = (Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id).resolve()
return output_repo_id, input_path, output_path
def get_output_path(
repo_id: str,
new_repo_id: str | None,
root: Path | str | None,
new_root: Path | str | None,
) -> tuple[str, Path]:
input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
output_repo_id = new_repo_id if new_repo_id else repo_id
output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
# In case of in-place modification, create a backup of the original dataset (if it exists)
if output_path == input_path:
@@ -557,7 +585,39 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
if not isinstance(cfg.operation, RecomputeStatsConfig):
raise ValueError("Operation config must be RecomputeStatsConfig")
dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
# Determine whether this is an in-place operation
output_repo_id, input_root, output_root = _resolve_io_paths(
cfg.repo_id,
cfg.new_repo_id,
cfg.root,
cfg.new_root,
default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
)
in_place = output_root == input_root
if in_place and not cfg.operation.overwrite:
raise ValueError(
f"recompute_stats would overwrite the dataset in-place at {input_root}. "
"Pass --operation.overwrite true to allow in-place modification, "
"or use --new_repo_id / --new_root to write to a different location. "
f"Default output repo_id when neither is set: '{cfg.repo_id}_recomputed_stats'."
)
if in_place:
logging.warning(
f"Overwriting dataset stats in-place at {input_root}. The original stats will be lost."
)
dataset = LeRobotDataset(cfg.repo_id, root=input_root)
else:
logging.info(f"Copying dataset from {input_root} to {output_root}")
if output_root.exists():
backup_path = output_root.with_name(output_root.name + "_old")
logging.warning(f"Output directory {output_root} already exists. Moving to {backup_path}")
if backup_path.exists():
shutil.rmtree(backup_path)
shutil.move(output_root, backup_path)
shutil.copytree(input_root, output_root)
dataset = LeRobotDataset(output_repo_id, root=output_root)
logging.info(f"Recomputing stats for {cfg.repo_id}")
if cfg.operation.relative_action:
@@ -578,7 +638,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
logging.info(f"Stats written to {dataset.root}")
if cfg.push_to_hub:
logging.info(f"Pushing to hub as {dataset.meta.repo_id}...")
logging.info(f"Pushing to hub as {dataset.repo_id}...")
dataset.push_to_hub()
+3 -1
View File
@@ -115,7 +115,9 @@ _feetech_sdk_available = is_package_available("feetech-servo-sdk", import_name="
_reachy2_sdk_available = is_package_available("reachy2_sdk")
_can_available = is_package_available("python-can", "can")
_unitree_sdk_available = is_package_available("unitree-sdk2py", "unitree_sdk2py")
_pyrealsense2_available = is_package_available("pyrealsense2")
_pyrealsense2_available = is_package_available("pyrealsense2") or is_package_available(
"pyrealsense2-macosx", import_name="pyrealsense2"
)
_zmq_available = is_package_available("pyzmq", import_name="zmq")
_hebi_available = is_package_available("hebi-py", import_name="hebi")
_teleop_available = is_package_available("teleop")
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c2b8f8532c7a0b776de5e536b8b54e30b1a0c2e3d5cc25a2d86fe43e40ae5e8c
oid sha256:8a31653c11eccdd4d80fd3f6a351cd54c49b8a48db1f7e9faf38fddd7900a09f
size 515400
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:224b5fa4828aa88171b68c036e8919c1eae563e2113f03b6461eadf5bf8525a6
oid sha256:75bf051698b37dcd7517ec8025a896ab5a0551a6dde5f89d0a3d5d50966e83e6
size 31672
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:016d2fa8fe5f58017dfd46f4632fdc19dfd751e32a2c7cde2077c6f95546d6bd
oid sha256:88e10930a10041d50f2cf369e6813ac14618d13dad1c21bdde1ac7798611c6ba
size 68
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:eca0d87a699620e4fec7e68539b0be91e4cc933f6bf12032da52c182ab6f38cf
oid sha256:89833a5ccdb7d85c83f717ff8ec68b8e822005cb8803899acaae88c578e2e3ae
size 31672
Generated
+257 -314
View File
@@ -2,30 +2,39 @@ version = 1
revision = 2
requires-python = ">=3.12"
resolution-markers = [
"python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
"python_full_version >= '3.15' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux'",
"python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
"python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
"(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version >= '3.15' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
"(python_full_version >= '3.15' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
"python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'emscripten'",
"python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'emscripten'",
"(python_full_version == '3.14.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"(python_full_version == '3.13.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
"python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
"(python_full_version < '3.13' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
"python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
"python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
"python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
"python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
"python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
"python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
"python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
"(python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32')",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
"(python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'win32')",
"python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'win32'",
"(python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'win32')",
"(python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32')",
"python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'win32'",
"python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
"(python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32')",
"python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
@@ -1110,8 +1119,7 @@ source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "absl-py" },
{ name = "dm-env" },
{ name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
{ name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
{ name = "dm-tree" },
{ name = "glfw" },
{ name = "labmaze" },
{ name = "lxml" },
@@ -1136,8 +1144,7 @@ version = "1.6"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "absl-py" },
{ name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
{ name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
{ name = "dm-tree" },
{ name = "numpy" },
]
sdist = { url = "https://files.pythonhosted.org/packages/62/c9/93e8d6239d5806508a2ee4b370e67c6069943ca149f59f533923737a99b7/dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de", size = 20187, upload-time = "2022-12-21T00:25:29.306Z" }
@@ -1149,22 +1156,11 @@ wheels = [
name = "dm-tree"
version = "0.1.9"
source = { registry = "https://pypi.org/simple" }
resolution-markers = [
"python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
"(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
"(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
"python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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