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About this paper
Yirui Sun, Guangyu Zhuge, Keliang Liu, Jie Gu, Shiqin Dai, Xinyu Bing, Zhongxue Gan, Chunxu Tian
A state-adaptive scheduler called SANTS significantly enhances World Action Models (WAMs) by dynamically controlling the video denoising process, achieving up to an 81.7% reduction in inference latency while improving task success rates to 94.4% in simulation and 73.1% on real-robot manipulation tasks. This approach demonstrates that optimal visual conditions for robot action generation are state-dependent and often do not require fully denoised video.
Key insights
- Action utility, measured by action error, exhibits diminishing returns and state dependence with increasing video denoising depth; full denoising is often not the optimal condition for action generation.
- Late-stage video denoising predictions can become physically inconsistent or less action-relevant, which can mislead action generation and introduce latency without improving performance.
- Dynamically adapting the video denoising process, by both stopping early and adjusting progression steps, is crucial for achieving a superior success-latency trade-off compared to fixed-schedule WAMs.
Summary by alphaXiv.
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