ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Reed Garrett

Keywords: robotic manipulation, reinforcement learning, imitation learning, data generation

Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community. We propose ReinforceGen, a system that combines task decomposition, data generation, imitation learning, and motion planning to form an initial solution, and improves each component through reinforcement-learning-based fine-tuning. ReinforceGen first segments the task into multiple localized skills, which are connected through motion planning. The skills and motion planning targets are trained with imitation learning on a dataset generated from 10 human demonstrations, and then fine-tuned through online adaptation and reinforcement learning. When benchmarked on the Robosuite dataset, ReinforceGen reaches 80% success rate on all tasks with visuomotor controls in the highest reset range setting. Additional ablation studies show that our fine-tuning approaches contributes to an 89% average performance increase. More results and videos available in https://sites.google.com/view/reinforce-gen

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Submission Number: 26