GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the uncertainty of video generation and the challenges of collecting large-scale robot datasets for training diffusion models. To address these limitations, we propose GenFlowRL, which derives shaped rewards from generated flow trained from diverse cross-embodiment datasets. This enables learning generalizable and robust policies from diverse demonstrations using low-dimensional, object-centric features. Experiments on 10 manipulation tasks, both in simulation and real-world cross-embodiment evaluations, demonstrate that GenFlowRL effectively leverages manipulation features extracted from generated object-centric flow, consistently achieving superior performance across diverse and challenging scenarios. Our Project Page: https://colinyu1.github.io/genflowrl
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningVideo GenerationSimilar Papers 제목 키워드 기반
Shaping embodied agent behavior with activity-context priors from egocentric video
Complex physical tasks entail a sequence of object interactions, each with its own preconditions -- which can be difficult for robotic agents to learn efficiently solely through their own experience. We introduce an appr…
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward. To tackle this problem, a common appr…
Real-Time Strategy GamesReinforcement Learning (RL)Advantage Shaping as Surrogate Reward Maximization: Unifying Pass@K Policy Gradients
This note reconciles two seemingly distinct approaches to policy gradient optimization for the Pass@K objective in reinforcement learning with verifiable rewards: (1) direct REINFORCE-style methods, and (2) advantage-sha…
Reinforcement LearningSearch-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-step reasoning. Agentic RAG addresses th…
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL). Existing approaches such as potential-based reward shaping normally make full use of a given shaping reward fu…
MuJoCoReinforcement Learning (RL)