7 citations · 8 across the 11 of their papers we have counts for
13 papers
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…
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,…
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 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…
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…
Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
Edgar Welte, Rania Rayyes
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are u…
Diffusion Models for Robotic Manipulation: A Survey
Rosa Wolf, Yitian Shi, Sheng Liu +1
Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation. They have also recently emerged as a promising approach in ro…
VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility
Yitian Shi, Di Wen, Guanqi Chen +5
We propose VISO-Grasp, a novel vision-language-informed system designed to systematically address visibility constraints for grasping in severely occluded environments. By leveragi…
AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
Claudius Kienle, Benjamin Alt, Finn Schneider +3
Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipul…
vMF-Contact: Uncertainty-aware Evidential Learning for Probabilistic Contact-grasp in Noisy Clutter
Yitian Shi, Edgar Welte, Maximilian Gilles +1
Grasp learning in noisy environments, such as occlusions, sensor noise, and out-of-distribution (OOD) objects, poses significant challenges. Recent learning-based approaches focus…