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

• URL: http://arxiv.org/abs/2512.16861v1
• Date: Thu, 18 Dec 2025 18:32:39 GMT
• Title: ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning
• Authors: Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Garrett,
• Abstract summary: ReinforceGen is a system that combines task decomposition, data generation, imitation learning, and motion planning.<n>It reaches 80% success rate on all tasks with visuomotor controls in the highest reset range setting.<n>Additional ablation studies show that our fine-tuning approaches contribute to an 89% average performance increase.
• Score: 32.137320056371784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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://reinforcegen.github.io/

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