6 papers
AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection
Mingi Choi, Gunhee Kim, Jisoo Kim +4
Learning robust dexterous grasping requires real-world data that records the physical outcomes of grasp attempts. Such data is hard to obtain at scale: teleoperation yields valid p…
ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning
Jisoo Kim, Sangwon Baik, Taeksoo Kim +6
We present ZeroDex, a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans from calibrated multi-view RGB im…
OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interaction
Junyoung Lee, Inhee Lee, Sookwan Han +9
Robots that assist people must respond promptly and appropriately to human activity, which requires low-latency processing, accurate perception, and anticipation of human motion. T…
HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
Jongbin Lim, Taeyun Ha, Mingi Choi +6
We present HRDexDB, a real-world 4D dexterous grasping dataset capturing 3D hand-object interaction trajectories over time across five embodiments. The dataset comprises 3.2K trial…
Text-Guided 6D Object Pose Rearrangement via Closed-Loop VLM Agents
Sangwon Baik, Gunhee Kim, Mingi Choi +1
Vision-Language Models (VLMs) exhibit strong visual reasoning capabilities, yet they still struggle with 3D understanding. In particular, VLMs often fail to infer a text-consistent…
Learning to Transfer Human Hand Skills for Robot Manipulations
Sungjae Park, Seungho Lee, Mingi Choi +4
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information…