Geometry Guided Self-Consistency for Physical AI
State-of-the-art physical AI models generate a chunk of actions per inference through diffusion or flow matching, iteratively refining an initial noise sample into an action trajectory. Because this inference process is inherently stochastic, committing to a single trajectory per round is brittle, and this brittleness compounds across the many sequential rounds that comprise a complete episode. We introduce KeyStone, an inference-time self-consistency method for diffusion-based action generation that draws $K$ candidate action chunks in parallel from a shared model context, clusters them in continuous action space, and returns the medoid of the largest cluster -- no additional model requi...
| Authors | Yinwei Dai, Zhuofu Chen, Lijie Yang et al. |
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| Primary category | cs.RO |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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