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Uni-PNP: Unified Prehensile and Non-prehensile Policy Learning for Generalizable Object Pose Reaching
Wenxuan Zhu, Yuze Wang, Jiangran Lyu +6
Object manipulation often requires combining non-prehensile actions with grasping. However, existing approaches typically develop these capabilities separately and integrate them b…
CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation
Yanchen Ding, Jiayi Chen, Jianfeng Xu +3
Efficient synthesis of contact-rich manipulation trajectories is essential for downstream robot learning. Gradient-based planning offers a promising approach, but jointly optimizin…
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
Long Xu, Zhiqi Zhang, Mi Yan +8
Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene ge…
KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
Yuansen Huang, Jiayi Chen, Haoran Liu +6
Generating high-quality dexterous grasps remains challenging for learning-based methods, which often depend on carefully tuned contact losses or costly contact-based test-time refi…
BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
Mu Lin, Yi-Lin Wei, Jiaxuan Chen +7
Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. I…
Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data
Zhikai Zhang, Haofei Lu, Yunrui Lian +12
Human athletes demonstrate versatile and highly-dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors…
TacDexGrasp: Compliant and Robust Dexterous Grasping with Tactile Feedback
Yubin Ke, Jiayi Chen, Hang Lv +2
Multi-fingered hands offer great potential for compliant and robust grasping of unknown objects, yet their high-dimensional force control presents a significant challenge. This wor…
GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization
Liangwang Ruan, Jiayi Chen, He Wang +1
Synthesizing high-quality dexterous grasps is a fundamental challenge in robot manipulation, requiring adherence to diversity, kinematic feasibility (valid hand-object contact with…