1 citations · 2 across the 11 of their papers we have counts for
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MS-MEM: Multi-Skill Manipulation-Enhanced Mapping via Uncertainty- and Disturbance-Aware Action Selection
Yitian Shi, Jesper Mücke, Nils Dengler +3
Accurate scene understanding in confined, cluttered spaces such as shelves is essential for service robots, as many everyday tasks require them to locate and retrieve objects relia…
Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization
Yitian Shi, Di Wen, Zhengqi Han +6
Learning from human video demonstrations remains challenging due to noisy hand-object interactions, unseen objects with partial observation, and cross-embodiment discrepancy. To ad…
Tracing Back Error Sources to Explain and Mitigate Pose Estimation Failures
Loris Schneider, Yitian Shi, Rosa Wolf +3
Robust estimation of object poses in robotic manipulation is often addressed using foundational general estimators, that aim to handle diverse error sources naively within a single…
FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation
Edgar Welte, Yitian Shi, Rosa Wolf +2
Generative manipulation policies can fail catastrophically under deployment-time distribution shift, yet many failures are near-misses: the robot reaches almost-correct poses and w…
Human-Interpretable Uncertainty Explanations for Point Cloud Registration
Johannes A. Gaus, Loris Schneider, Yitian Shi +3
In this paper, we address the point cloud registration problem, where well-known methods like ICP fail under uncertainty arising from sensor noise, pose-estimation errors, and part…
HOGraspFlow: Taxonomy-Aware Hand-Object Retargeting for Multi-Modal SE(3) Grasp Generation
Yitian Shi, Zicheng Guo, Rosa Wolf +2
We propose Hand-Object\emph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw g…