1 citations · 2 across the 12 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…
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…
IMPACT: A Dataset for Multi-Granularity Human Procedural Action Understanding in Industrial Assembly
Di Wen, Zeyun Zhong, David Schneider +18
We introduce IMPACT, a synchronized five-view RGB-D dataset for deployment-oriented industrial procedural understanding, built around real assembly and disassembly of a commercial…
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…
Go Beyond Earth: Understanding Human Actions and Scenes in Microgravity Environments
Di Wen, Lei Qi, Kunyu Peng +9
Despite substantial progress in video understanding, most existing datasets are limited to Earth's gravitational conditions. However, microgravity alters human motion, interactions…
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…
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…
Uncertainty-driven Exploration Strategies for Online Grasp Learning
Yitian Shi, Philipp Schillinger, Miroslav Gabriel +4
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i.e., o…