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arXiv · 2512.16861

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

Zihan Zhou·Animesh Garg·Ajay Mandlekar·Caelan Garrett
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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 contribute to an 89% average performance increase. Finally, ReinforceGen demonstrates significant improvement through fine-tuning in our real-world evaluations. More results and videos are available at https://reinforcegen.github.io.

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Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Garrett. 2025-12-18. ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning. https://arxiv.org/abs/2512.16861

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