🤖 AI Summary
This work addresses the inference latency inherent in World Action Models caused by fixed-horizon action chunk generation, which leads to execution pauses, outdated actions, and discontinuous trajectories in robotic systems. To mitigate these issues, the authors propose an asynchronous deployment strategy that overlaps model inference with action execution, thereby enhancing system responsiveness and motion smoothness. Through systematic evaluation of six deployment strategies, the study finds that action blending alone is insufficient to eliminate discontinuities at chunk boundaries. In contrast, prefix-conditioned generation—by precisely aligning observation, prediction, and execution timelines—achieves superior overall performance across dynamic manipulation, precise placement, and long-horizon tasks, striking an optimal balance among task success, execution speed, and trajectory smoothness.
📝 Abstract
World Action Models generate fixed-horizon action chunks through iterative denoising, creating substantial inference latency that can cause pauses, stale actions, and discontinuities during robotic execution. We present an empirical study of asynchronous deployment strategies that overlap model inference with action execution to enable responsive and smooth control. We compare six strategies, including synchronous execution, pure asynchronous switching, post-hoc action blending, denoising-time blending, inference-time velocity guidance, and prefix-conditioned generation, on a 10 Hz bimanual robot. Evaluation combines offline trajectory analysis with online experiments across dynamic manipulation, precision-critical placement, and long-horizon tasks. Our results identify accurate temporal alignment between observations, predictions, and executed commands as a fundamental requirement. Alignment errors produce persistent chunk-boundary discontinuities that cannot be corrected through blending alone. With proper alignment, direct action weighting provides a simple and smooth baseline but sacrifices accuracy in precision-critical tasks. Inference-time velocity guidance fails to reliably constrain committed actions on our platform. In contrast, prefix-conditioned generation achieves the best overall balance between task performance, execution speed, and trajectory smoothness by learning consistent action continuations during training. These findings clarify the practical trade-offs among asynchronous deployment strategies and provide guidance for deploying high-latency World Action Models in real-time robotic systems.