人間の選好で拡散ポリシーを微調整するFDPP
FDPP: Fine-tune Diffusion Policy with Human Preference
選好ベース学習で報酬関数を獲得し、強化学習で事前学習済み拡散ポリシーを微調整することで、元のタスク性能を保ちつつ人間の新たな選好に行動を適応させる手法を提案。
著者: Yuxin Chen, Devesh K. Jha, Masayoshi Tomizuka, Diego Romeres
分類: cs.RO, cs.LG
原文アブストラクト
Imitation learning from human demonstrations enables robots to perform complex manipulation tasks and has recently witnessed huge success. However, these techniques often struggle to adapt behavior to new preferences or changes in the environment. To address these limitations, we propose Fine-tuning Diffusion Policy with Human Preference (FDPP). FDPP learns a reward function through preference-based learning. This reward is then used to fine-tune the pre-trained policy with reinforcement learning (RL), resulting in alignment of pre-trained policy with new human preferences while still solving the original task. Our experiments across various robotic tasks and preferences demonstrate that FDPP effectively customizes policy behavior without compromising performance. Additionally, we show that incorporating Kullback-Leibler (KL) regularization during fine-tuning prevents over-fitting and helps maintain the competencies of the initial policy.
