Author

Yitian Shi

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Preprint Sep 2026

MS-MEM: Multi-Skill Manipulation-Enhanced Mapping via Uncertainty- and Disturbance-Aware Action Selection

Experimental results show that, compared with single-skill and unconstrained baselines that ignore scene disturbance, MS-MEM achieves higher mapping accuracy while substantially reducing scene disturbance, highlighting the synergistic effects of active viewpoint selection, push, and grasp actions.

Yi-Tian Shi, Jesper Mücke, Nils Dengler et al. · 0 citations

#machine learning Feb 2026

FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation

The results clearly demonstrate that FlowCorrect learns from very few demonstrations and enables fast, sample-efficient, incremental, human-in-the-loop corrections of generative visuomotor policies at deployment time in real-world robotics.

Edgar Welte, Yitian Shi, R. Wolf et al. · arXiv.org · 4 citations