Learning Where it Matters: Autonomous Data Collection and Offline RL for Local-2
Overview
This research proposes a modular approach that combines autonomous data collection with offline reinforcement learning to improve performance in contact-rich manipulation tasks. By collecting dense data locally and integrating it with traditional motion planning, the method achieves a 96% success rate in long-horizon tasks, outperforming planning-only and end-to-end baselines. The approach also reduces applied forces by 49% and maintains 90% performance in out-of-distribution scenarios.
From arXiv Robotics — research abstracts
Event evidence and corrections
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“ight-tolerance tasks. To reliably solve challenging contact-rich tasks, we propose to leverage robot data and learning where it matters in a modular approach. First, we introduce an autonomous data collection scheme for the real world that collects dense data locally in the contact-rich segment of the task, eliminating reliance on a teleoperator's skill. Second, we show that using offline RL, the collected data can b”
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Reported quantity · Reported success rate: 96 percent · Basis not reported
“e translated into policies with high success rates. We then integrate our local policies with traditional motion planning to solve long-horizon assembly tasks. Across four real-world tasks, using only 2-2.5 h of autonomous data collection, we achieve an average success rate of 96% for long-horizon tasks, compared to 55% for a planning-only baseline and 37% for the strongest end-to-end baseline. Our local policies als”
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Reported quantity · real-world evaluation rollouts: 1500 other · Basis not reported
“o reduce applied forces by 49%. Notably, performance remains at 90% for scene-level out-of-distribution scenarios, while end-to-end approaches struggle. In total, we run over 1500 real-world evaluation rollouts. Our results pave the way for targeted data collection in contact-rich tasks and for high success rates in precision applications. Videos and qualitative results are available at https://learnsyslab.github.io/”
Exact source · revision 1Source owner not reported
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- 2026-10-30T21:57:19.739Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T21:57:09.768Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalLearning Where it Matters: Autonomous Data Collection and Offline Reinforcement Learning for Local Contact-Rich Manipulation
This research proposes a modular approach that combines autonomous data collection with offline reinforcement learning to improve performance in contact-rich manipulation tasks. By collecting dense data locally and integrating it with traditional motion planning, the method achieves a 96% success rate in long-horizon tasks, outperforming planning-only and end-to-end baselines. The approach also reduces applied forces by 49% and maintains 90% performance in out-of-distribution scenarios.
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