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Long-WAM: Scaling the Context of World-Action Models

Long-WAM scales robot world-action context, lifting RoboCasa success from 63.3% to 78.7%.

Long-WAM scales the context of causal world-action models under real-time robot-control constraints. On RoboCasa GR-1, extending context from 0 to 19.2 seconds raises success from 63.3% to 78.7% when the video foundation is pretrained autoregressively, while a bidirectional initialization shows no net gain. It reports the best compared results on LIBERO-Long, RoboTwin 2.0, and DOMINO, with each action chunk taking 107.4 ms on an RTX 5090. Deployed on Unitree G1 and YAM, it reaches 95% success on dynamic cup stacking, where Pi0.5 and Fast-WAM succeed in none of 20 trials.

arXiv cs.AI / cs.LG / cs.CLOct 7, 17:58 UTC · 1d agoAI research 3 sources1

Long-WAM: Scaling the Context of World-Action Models

Long-WAM raises robot task success by scaling causal world-action context with autoregressive video pretraining.

Long-WAM scales the context of causal world-action models for real-time robot control. Longer visual histories help mainly when the video foundation is pretrained autoregressively: on RoboCasa GR-1, context from 0.0 to 19.2 seconds raises success from 63.3% to 78.7%, while a bidirectional initialization shows no net gain. Long-WAM leads compared methods on LIBERO-Long, RoboTwin 2.0, and DOMINO, and reaches 95% success on dynamic cup stacking, where Pi0.5 and Fast-WAM succeed in none of 20 trials. On an RTX 5090, each action chunk, including future-video latent prediction, takes 107.4 ms, with deployment also shown on DGX Spark, Jetson AGX Thor, Unitree G1, and YAM.

Hugging Face daily papersupdated Oct 7, 17:58 UTC · 1d agofirst Oct 6, 20:00 UTC · 2d agoAI research 3 sources1

RealtimeWAM: One-Step Asynchronous World Action Models

RealtimeWAM enables one-step action generation and asynchronous inference, with about 25x speedup and under 1% drop.

World Action Models reuse video representations for action prediction, but multi-step denoising and sequential expert execution slow inference. RealtimeWAM uses Teacher-Anchored Consistency Distillation for accurate one-step actions and Cross-Expert Wavefront Pipelining to overlap the experts via block-wise KV-cache sharing. On LIBERO, LIBERO-Plus, and RoboTwin, variants including Fast-WAM and Faster-WAM lose under 1% performance while reaching about 25x end-to-end speedup on an H100. Code and checkpoints are released.

Hugging Face daily papersupdated Oct 7, 17:58 UTC · 1d agofirst Oct 4, 20:00 UTC · 4d agoAI research 3 sources

DeltaWAM: Delta World Action Models for Bimanual Manipulation

DeltaWAM predicts visual deltas and robot actions, raising RoboTwin success while cutting training and inference cost.

World-action models transfer video-generator priors to robot control but waste compute predicting largely unchanged future frames. DeltaWAM jointly predicts visual deltas and actions with dense-anchor, sparse-delta, and action streams, and Streaming Delta Memory updates cached anchors from compact observed deltas. On RoboTwin, DeltaWAM with SDM lifts average success over Fast-WAM from 81.3% to 85.4% clean and from 75.8% to 83.9% under visual randomization. The three architectures cut training FLOPs by 17.78-23.77%, while SDM cuts one-step inference latency 36.57% and FLOPs 31.55%.

Hugging Face daily papersSep 22, 20:00 UTC · 16d agoAI research

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Long-WAM: Scaling the Context of World-Action Models

Long-WAM: Scaling the Context of World-Action Models

RealtimeWAM: One-Step Asynchronous World Action Models

DeltaWAM: Delta World Action Models for Bimanual Manipulation