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Pepijn
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Port the LingBot-VA policy (Wan2.2 dual-stream video+action world model) into LeRobot, following the EO-1 / VLA-JEPA conventions. Covers inference, checkpoint conversion, and predicted-video saving (training is deferred to a follow-up PR). - Vendored Wan transformer/attention/flex/VAE/scheduler modules (key names preserved for near-identity conversion); torch SDPA default, flashattn/flex lazy-guarded. - LingBotVAConfig (registered "lingbot_va") + processor with fixed-quantile action unnormalization; full dual-stream sampling loop with CFG, two flow-matching schedulers and KV cache, mapped onto select_action with observed-keyframe feedback. - convert_lingbot_va_checkpoints.py (libero/robotwin variants): bundles the ~5B transformer, lazy-pulls the frozen VAE+UMT5 from the source repo. - Predicted-video plumbing in lerobot_eval (predicted_frames_callback; opt-in via --policy.save_predicted_video) and ConstantWithWarmupSchedulerConfig. - pyproject: widen diffusers-dep to <0.37, add lingbot_va + imageio-dep extras, add lingbot_va and (missing) eo1 to `all`. - Factory + policies/__init__ wiring, docs page + toctree, and tests. Note: the LIBERO success-rate correctness gate must be validated on a CUDA GPU with the converted checkpoint. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
121 lines
5.2 KiB
Plaintext
121 lines
5.2 KiB
Plaintext
# LingBot-VA
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LingBot-VA is an **autoregressive video-action world-model policy** built on the **Wan2.2**
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video-diffusion stack. It interleaves, in one autoregressive sequence, the prediction of
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future **video latents** and **robot actions** ("VA" = Video-Action). The LeRobot
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integration wires LingBot-VA into the standard training, evaluation and processor
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interfaces.
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## Model Overview
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LingBot-VA is a **dual-stream "mixture-of-transformers"**: a video/latent stream
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(`patch_embedding_mlp → blocks → proj_out`) and an action stream
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(`action_embedder → blocks → action_proj_out`) share the same 30 transformer blocks and
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text conditioning. Actions are produced by the dedicated `action_proj_out` head — they are
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**not** decoded from predicted pixels, though video and action are co-trained.
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| Component | Class | Role |
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| DiT backbone (trainable) | `WanTransformer3DModel` | ~5B-param dual-stream transformer (the only weights stored in the LeRobot checkpoint). |
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| VAE (frozen) | `AutoencoderKLWan` | Wan2.2 VAE, `z_dim=48`. Lazy-pulled from the source repo. |
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| Text encoder (frozen) | `UMT5EncoderModel` | UMT5-XXL, `d_model=4096`. Lazy-pulled from the source repo. |
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At inference the policy runs an autoregressive loop per chunk: it denoises the video-latent
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stream (CFG, ~20 steps) and the action stream (~50 steps) with two independent
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flow-matching schedulers, maintaining a KV cache across chunks. Real observed keyframes are
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fed back into the KV cache as the chunk is executed (closed-loop world modeling).
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### What the LeRobot Integration Covers
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- Standard `policy.type=lingbot_va` configuration through LeRobot.
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- Checkpoint conversion from the released HuggingFace checkpoints.
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- Autoregressive dual-stream inference behind the standard `select_action` interface
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(single-environment eval, `--eval.batch_size=1`).
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- Opt-in saving of the policy's **predicted (imagined) videos** during eval / training.
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- Evaluation with `lerobot-eval` on the LIBERO benchmark.
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Training (the flow-matching dual-stream loss + latent dataset) is part of a follow-up
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training port and is not yet wired into `lerobot-train`.
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## Installation
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1. Install LeRobot by following the [Installation Guide](./installation).
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2. Install the LingBot-VA extra (brings in `diffusers>=0.36` for the Wan2.2 stack):
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```bash
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pip install -e ".[lingbot_va]"
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# For LIBERO evaluation (Linux only):
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pip install -e ".[lingbot_va,libero]"
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```
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## Checkpoint Conversion
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The released checkpoints are diffusers-style directories
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(`robbyant/lingbot-va-base`, `robbyant/lingbot-va-posttrain-robotwin`,
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`robbyant/lingbot-va-posttrain-libero-long`). Convert one to LeRobot format with:
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```bash
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python -m lerobot.policies.lingbot_va.convert_lingbot_va_checkpoints \
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--checkpoint robbyant/lingbot-va-posttrain-libero-long \
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--variant libero \
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--output_dir outputs/lingbot_va_libero_long
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```
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**Packaging:** only the trainable ~5B transformer is stored in the LeRobot
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`model.safetensors`. The frozen VAE + UMT5 + tokenizer (~20 GB) are **lazily pulled** from
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`config.wan_pretrained_path` at load time (defaults to the source repo). Pass
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`--bundle-frozen` to copy those sub-folders next to the converted checkpoint instead.
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Run conversion on a Linux machine with a CUDA GPU and enough RAM/VRAM to materialize the
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transformer.
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## Evaluation (LIBERO)
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```bash
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lerobot-eval \
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--policy.path=outputs/lingbot_va_libero_long \
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--env.type=libero --env.task=libero_10 \
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--eval.n_episodes=50 --eval.batch_size=1 \
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--output_dir=outputs/eval/lingbot_va_libero
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```
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LingBot-VA's streaming inference (KV cache + observed-keyframe feedback) is implemented for
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single-environment eval; use `--eval.batch_size=1`.
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### Saving predicted (imagined) videos
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Set `--policy.save_predicted_video=true` to additionally VAE-decode the predicted video
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latents and write `pred_episode_*.mp4` next to the env-rendered `eval_episode_*.mp4` videos.
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The same flag works for the periodic eval during `lerobot-train`.
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## Inference Hyperparameters (LIBERO)
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| Key | Value |
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| height × width | 128 × 128 |
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| cameras | `observation.images.image` (agentview), `observation.images.image2` (eye-in-hand) |
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| action channels used | 0–6 (7-DoF arm + gripper) |
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| action_per_frame / frame_chunk_size | 4 / 4 |
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| attn_window | 30 |
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| video / action denoising steps | 20 / 50 |
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| guidance_scale / action_guidance_scale | 5 / 1 |
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| snr_shift / action_snr_shift | 5.0 / 0.05 |
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These are the defaults of `LingBotVAConfig`; override any of them via `--policy.<name>=...`.
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## Notes & Limitations
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- **Correctness gate:** matching the upstream LIBERO success rate requires validating the
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converted checkpoint on a GPU and tensor-diffing intermediate activations against the
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upstream implementation. The most sensitive parts are the action quantile normalization,
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the camera ordering, the `action_per_frame`/`frame_chunk_size` alignment, and `attn_mode`.
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- **Attention backend:** inference uses the `torch` SDPA backend (always available). The
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`flashattn` and `flex` backends are optional; `flex` is only needed for training.
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- **Model size:** the DiT is ~5B params and the frozen VAE+UMT5 add ~20 GB; inference needs
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roughly 18–24 GB of VRAM.
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## License
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LingBot-VA is released under Apache-2.0. See the
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[upstream repository](https://github.com/Robbyant/lingbot-va).
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