Diffusion Reward: Learning Rewards via Conditional Video Diffusion

• URL: http://arxiv.org/abs/2312.14134v3
• Date: Fri, 9 Aug 2024 03:06:42 GMT
• Title: Diffusion Reward: Learning Rewards via Conditional Video Diffusion
• Authors: Tao Huang, Guangqi Jiang, Yanjie Ze, Huazhe Xu,
• Abstract summary: Diffusion Reward is a framework that learns rewards from expert videos via conditional video diffusion models. We show the efficacy of our method over robotic manipulation tasks in both simulation platforms and the real world with visual input.
• Score: 26.73119637442011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning (RL) tasks. In this work, we propose Diffusion Reward, a novel framework that learns rewards from expert videos via conditional video diffusion models for solving complex visual RL problems. Our key insight is that lower generative diversity is exhibited when conditioning diffusion on expert trajectories. Diffusion Reward is accordingly formalized by the negative of conditional entropy that encourages productive exploration of expert behaviors. We show the efficacy of our method over robotic manipulation tasks in both simulation platforms and the real world with visual input. Moreover, Diffusion Reward can even solve unseen tasks successfully and effectively, largely surpassing baseline methods. Project page and code: https://diffusion-reward.github.io.

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