PAC-DP: PACベイズ拡散ポリシー学習
PAC-DP: PAC-Bayesian Diffusion Policy Learning
拡散ポリシーの訓練にPACベイズ理論に基づく正則化を導入し、ロボット操作タスクでの性能を向上させる手法を提案した論文。
詳しい要約
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著者: Mohammad Hasan Yeganegi, Dian Yu, Andrea Del Prete, Majid Khadiv, Matteo Saveriano
分類: cs.RO
原文アブストラクト
Diffusion Policies (DPs) are able to perform complex manipulation tasks. However, DPs are typically trained by minimizing a denoising objective, which provides limited control over generalization in the finite-data regimes common in robotics. In this letter, we propose PAC-DP, an approach that increases the performance of DPs in robotic manipulation tasks. By modeling the DP as a Bayesian neural network, and defining a PAC-Bayes generalization bound, we derive a novel training objective that augments the standard denoising loss with a Kullback-Leibler divergence regularizer between the posterior and prior parameter distributions. From the theoretical perspective, our approach provides a principled approach to regularize the training of DPs without significantly increasing the training time. From the practical point of view, experimental results demonstrate improved denoising performance, lower variational negative log-likelihood, and higher success rates across multiple robotic manipulation benchmarks. Crucially, the largest improvements are observed in low-data training regimes and complex tasks, establishing PAC-DP as a theoretically grounded framework for robot policy learning.
