activity
20242026
most citedInteractive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey

7 citations · 8 across the 11 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.RO2026

INSPECT: Learning Robot View Selection from Assistant Use

Di Wen, Kailun Yang, Wenhao Guo +7

Robots inspecting an assembly must determine which parts are present and whether they are correctly installed. During egocentric assembly assistance, head motion and workpiece hand…

cs.RO2026

RAFAIL: Relationship-Aware Failure Detection for Robotic Manipulation

Loris Schneider, Edgar Welte, Rania Rayyes

Detecting failures during execution is essential for reliable robotic manipulation. Vision-language models (VLMs) can assess task outcomes semantically but add runtime computation,…

cs.RO2026

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…

cs.RO2026

Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Object Category-agnostic Temporal Localization

Yitian Shi, Di Wen, Zhengqi Han +6

Human videos provide a scalable source of demonstrations for robot imitation learning. However, converting them into executable and semantically aligned robot trajectories requires…

cs.RO2026

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…

cs.RO2026

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…