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Autonomous Data Collection and Offline Reinforcement Learning for Local Contact-Rich Manipulation Tasks

The paper proposes a modular method that autonomously collects data in local segments of contact-rich tasks and uses offline reinforcement learning to train local policies, which are then integrated with traditional motion planning to complete long-horizon assembly tasks. Across four real-world tasks, using only 2-2.5 hours of autonomous data collection, the method achieved an average success rate of 96% on long-horizon tasks, compared with 55% for a planning-only baseline and 37% for the strongest end-to-end baseline; the local policies also reduced applied force by 49%. Performance remained at 90% in scene-level out-of-distribution scenarios, with more than 1,500 real-world evaluation rollouts completed in total.

Part of the event: Autonomous Data Collection and Offline Reinforcement Learning Method for Local Contact-Rich Manipulation

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