Explicitly capturing demonstrator multi-modality for distillation into diffusion policies
Overview
This paper addresses the need for exploration in distilled diffusion policies by adapting vine sampling to behavior cloning. The authors query demonstrators multiple times at identical observation states to resolve ambiguity between unimodal memorization and true multi-modal distribution learning. Experiments on synthetic tasks and a 2D truck unloading scenario show that increasing the number of actions per observation ($k$) yields policies that better match demonstrator distributions, improving best-of-$N$ and expert iteration performance.
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- arXiv Robotics — research abstractsSignalExplicitly capturing demonstrator multi-modality for distillation into diffusion policies
This paper addresses the need for exploration in distilled diffusion policies by adapting vine sampling to behavior cloning. The authors query demonstrators multiple times at identical observation states to resolve ambiguity between unimodal memorization and true multi-modal distribution learning. Experiments on synthetic tasks and a 2D truck unloading scenario show that increasing the number of actions per observation ($k$) yields policies that better match demonstrator distributions, improving best-of-$N$ and expert iteration performance.
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