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pepijn223
e3deff00ad
Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the package to LeRobot conventions. - Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers, flow-matching scheduler, grid utils, flex-attention) into a single modeling_lingbot_va.py; remove the separate wan_*/schedulers modules. - Move the fixed action (un)normalization quantiles out of the config and into the post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in favour of ready-to-use LeRobot-format checkpoints on the Hub. - Fixes found via on-sim validation: undo LIBERO's 180-degree image flip (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen VAE/UMT5 by subfolder with the text encoder on CPU. - RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK) and the robotwin_tshape latent layout (full-res head + half-res wrists via a second streaming VAE) with the upstream RoboTwin action quantiles + camera mapping. - Predicted-video saving works for both benchmarks; docs + tests updated. Co-authored-by: Cursor <cursoragent@cursor.com>
138 lines
6.1 KiB
Plaintext
138 lines
6.1 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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- Ready-to-use LeRobot-format checkpoints on the Hub (converted from the released upstream ones).
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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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## Checkpoints
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The released upstream checkpoints have been converted to LeRobot format and pushed to the Hub:
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| Variant | LeRobot checkpoint |
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| LIBERO-Long post-train | `pepijn223/lingbot_va_libero_long` |
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| RoboTwin post-train | `pepijn223/lingbot_va_robotwin` |
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| Pretrained base | `pepijn223/lingbot_va_base` |
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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 `robbyant/*` repo). The
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UMT5-XXL text encoder runs on CPU by default (`config.text_encoder_device`) so the 5B
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transformer + VAE fit on a single 24–32 GB GPU.
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## Evaluation (LIBERO)
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```bash
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lerobot-eval \
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--policy.path=pepijn223/lingbot_va_libero_long \
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--policy.device=cuda \
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--env.type=libero --env.task=libero_10 \
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--env.observation_height=128 --env.observation_width=128 \
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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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Native LeRobot eval reproduces **96% success on `libero_10` (LIBERO-Long)** (48/50 episodes).
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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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## Evaluation (RoboTwin)
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RoboTwin 2.0 needs the SAPIEN + CuRobo simulator stack — use the benchmark Docker image
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(`docker/Dockerfile.benchmark.robotwin`, which also needs `warp-lang==1.3.1` and CuRobo built
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with the GPU's compute capability in `TORCH_CUDA_ARCH_LIST`). RoboTwin uses **end-effector-pose
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control**, so run with `--env.action_mode=ee`: the policy predicts per-arm `xyz+quaternion+gripper`
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deltas (`robotwin_tshape` latent layout) that are composed onto the episode's initial eef pose and
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executed via CuRobo IK.
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```bash
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lerobot-eval \
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--policy.path=pepijn223/lingbot_va_robotwin \
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--policy.device=cuda \
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--env.type=robotwin --env.task=beat_block_hammer --env.action_mode=ee \
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--eval.n_episodes=10 --eval.batch_size=1 \
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--output_dir=outputs/eval/lingbot_va_robotwin
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```
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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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