LingBot-VA

LingBot-VA is an autoregressive video-action world-model policy built on the Wan2.2 video-diffusion stack. It interleaves, in one autoregressive sequence, the prediction of future video latents and robot actions (“VA” = Video-Action). The LeRobot integration wires LingBot-VA into the standard training, evaluation and processor interfaces.

Model Overview

LingBot-VA is a dual-stream “mixture-of-transformers”: a video/latent stream (patch_embedding_mlp → blocks → proj_out) and an action stream (action_embedder → blocks → action_proj_out) share the same 30 transformer blocks and text conditioning.

At inference the policy runs an autoregressive loop per chunk: it denoises the video-latent stream (CFG, ~20 steps) and the action stream (~50 steps) with two independent flow-matching schedulers, maintaining a KV cache across chunks. Real observed keyframes are fed back into the KV cache as the chunk is executed (closed-loop world modeling).

What the LeRobot Integration Covers

• Standard policy.type=lingbot_va configuration through LeRobot.
• Ready-to-use LeRobot-format checkpoints on the Hub (converted from the released upstream ones).
• Autoregressive dual-stream inference behind the standard select_action interface (single-environment eval, --eval.batch_size=1).
• Opt-in saving of the policy’s predicted (imagined) videos during eval / training.
• Evaluation with lerobot-eval on LIBERO and RoboTwin.
• Training / fine-tuning via the dual-stream flow-matching loss (policy.forward), see below.

Installation

1. Install LeRobot by following the Installation Guide.
2. Install the LingBot-VA extra:

pip install -e ".[lingbot_va]"

Checkpoints

The released upstream checkpoints have been converted to LeRobot format and pushed to the Hub:

Only the trainable ~5B transformer is stored in the LeRobot model.safetensors. The frozen VAE + UMT5 + tokenizer (~20 GB) are pulled from config.wan_pretrained_path at load time (defaults to the source robbyant/* repo). The UMT5-XXL text encoder runs on CPU by default (config.text_encoder_device) so the 5B transformer + VAE fit on a single 24–32 GB GPU.

Evaluation (LIBERO)

lerobot-eval \
    --policy.path=lerobot/lingbot_va_libero_long \
    --policy.device=cuda \
    --env.type=libero --env.task=libero_10 \
    --env.observation_height=128 --env.observation_width=128 \
    --eval.n_episodes=50 --eval.batch_size=1 \
    --output_dir=outputs/eval/lingbot_va_libero

LingBot-VA’s streaming inference (KV cache + observed-keyframe feedback) is implemented for single-environment eval; use --eval.batch_size=1.

Evaluation (RoboTwin)

RoboTwin 2.0 needs the SAPIEN + CuRobo simulator stack. You can use the benchmark Docker image (docker/Dockerfile.benchmark.robotwin, which also needs warp-lang==1.3.1 and CuRobo built with the GPU’s compute capability in TORCH_CUDA_ARCH_LIST). RoboTwin uses end-effector-pose control, so run with --env.action_mode=ee: the policy predicts per-arm xyz+quaternion+gripper deltas (robotwin_tshape latent layout) that are composed onto the episode’s initial eef pose and executed via CuRobo IK.

lerobot-eval \
    --policy.path=lerobot/lingbot_va_robotwin \
    --policy.device=cuda \
    --env.type=robotwin --env.task=beat_block_hammer --env.action_mode=ee \
    --eval.n_episodes=10 --eval.batch_size=1 \
    --output_dir=outputs/eval/lingbot_va_robotwin

Saving predicted (imagined) videos

Set --policy.save_predicted_video=true to additionally VAE-decode the predicted video latents and write pred_episode_*.mp4 next to the env-rendered eval_episode_*.mp4 videos. The same flag works for the periodic eval during lerobot-train.

Training / fine-tuning

LingBotVAPolicy.forward(batch) implements the dual-stream flow-matching loss (latent_loss + action_loss, timestep-weighted, action-masked) from the paper: it VAE-encodes the camera clips into video latents, UMT5-encodes the task, noises both streams, runs the transformer’s block-causal training pass and returns (loss, metrics). Optimizer preset is AdamW with a linear-warmup-then-constant schedule (matching upstream).

Requirements:

• The block-causal masks use PyTorch flex-attention, so build the policy with --policy.attn_mode=flex for training (the default torch SDPA is inference-only).
• The full 5B DiT does not fit a single 24–32 GB GPU under AdamW; fine-tune with LoRA (--policy.use_peft=true) and/or optimizer offload. get_optim_params returns only the trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen.

lerobot-train \
  --policy.path=lerobot/lingbot_va_libero_long --policy.attn_mode=flex \
  --policy.use_peft=true \
  --dataset.repo_id=<your LeRobot-format dataset> \
  --batch_size=1 --steps=... --output_dir=outputs/train/lingbot_va

The dataset must provide camera clips (a temporal window per camera, VAE-encoded to frame_chunk_size latent frames) and frame_chunk_size * action_per_frame action steps per item.

Data format (action channels & camera order)

LingBot-VA is an end-effector (Cartesian) pose policy, it predicts EEF poses + gripper, not joint positions. Actions live in a fixed multi-embodiment 30-dim layout; map your robot’s action dimensions into these channels and pad the rest with 0 (used_action_channel_ids selects the channels a given checkpoint actually uses):

• LIBERO uses channels 0–6: a 6-DoF EEF delta (xyz + rotation) + gripper (single arm).
• RoboTwin uses channels [0–6, 28, 7–13, 29]: left EEF (xyz + quaternion) + left gripper + right EEF + right gripper (16 dims). The env converts these poses to joint trajectories via CuRobo IK — joints are never predicted.

Joint-space datasets (or a different EEF convention) must be remapped into this schema before fine-tuning these checkpoints.

Camera order is fixed and order-sensitive, per-camera latents are concatenated spatially in obs_cam_keys order, so the physical camera→slot mapping must match training:

The first camera is the exterior/head view and the rest are wrist views.

Inference Hyperparameters (LIBERO)

These are the defaults of LingBotVAConfig; override any of them via --policy.<name>=....

Notes

• Attention backend: inference uses the torch SDPA backend (always available). The flashattn and flex backends are optional; flex is only needed for training.
• Model size: the DiT is ~5B params and the frozen VAE+UMT5 add ~20 GB; inference needs roughly 18–24 GB of VRAM.

License

LingBot-VA is released under Apache-2.0. See the upstream repository.