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arXiv · 2607.15982

Data and Learning Where it Matters for Contact-Rich Manipulation

Oliver Hausdörfer·Linus Schwarz·Gabor Marko·Christian Dietz·Timo Class·Luka Hofer·Jim Yun-Jin Li·Johannes Hechtl·Ralf Römer·Angela P. Schoellig
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Abstract

Learned policies trained end-to-end on large datasets often remain brittle in high-precision tasks and struggle with generalization. We find that these limitations largely stem from a lack of structure and focus in data collection. Our key insight is to leverage dense data collection only for the critical segment of contact-rich tasks and to rely on traditional planning during simple free-space motion. We propose an automated data-collection scheme in combination with offline deep reinforcement learning for the critical segment of the task, eliminating reliance on a teleoperator's skill and on online policy updates. Across four challenging real-world tasks, using only 2 to 2.5 hours of autonomous data collection, we achieve an average success rate of 96%, compared to the strongest baseline at 55%. Notably, performance remains high in out-of-distribution scenarios where end-to-end approaches struggle. Our results pave the way for targeted data collection for contact-rich tasks and for high success rates in precision applications.

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Oliver Hausdörfer, Linus Schwarz, Gabor Marko, Christian Dietz, Timo Class, Luka Hofer, Jim Yun-Jin Li, Johannes Hechtl, Ralf Römer, Angela P. Schoellig. 2026-07-17. Data and Learning Where it Matters for Contact-Rich Manipulation. https://arxiv.org/abs/2607.15982

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