13 citations · 14 across the 7 of their papers we have counts for
6 papers · 1 filter
MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning
Qijin She, Hanyang Yu, Zeming Li +1
In-context imitation learning enables few-shot policy generalization but struggles to maintain performance on unseen objects and novel scenarios. To address this, we introduce Matc…
Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization
Wun Lam Yeung, Wenjun Liu, Yui Cheung Yu +5
Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial gr…
LLM-enhanced Scene Graph Learning for Household Rearrangement
Wenhao Li, Zhiyuan Yu, Qijin She +5
The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places. It depends both on common-sense knowledge on the objective…
Learning Cross-hand Policies for High-DOF Reaching and Grasping
Qijin She, Shishun Zhang, Yunfan Ye +2
Reaching-and-grasping is a fundamental skill for robotic manipulation, but existing methods usually train models on a specific gripper and cannot be reused on another gripper. In t…
Learning Dual-arm Object Rearrangement for Cartesian Robots
Shishun Zhang, Qijin She, Wenhao Li +4
This work focuses on the dual-arm object rearrangement problem abstracted from a realistic industrial scenario of Cartesian robots. The goal of this problem is to transfer all the…
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
Qijin She, Ruizhen Hu, Juzhan Xu +3
We approach the problem of high-DOF reaching-and-grasping via learning joint planning of grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue i…