7 papers
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Xinghao Zhu, Zixi Liu, Shalin Jain +18
Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact…
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
Residual Policy Gradient: A Reward View of KL-regularized Objective
Pengcheng Wang, Xinghao Zhu, Yuxin Chen +3
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the ad…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking
Yongpeng Jiang, Mingrui Yu, Xinghao Zhu +2
Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications.…
Adaptive Energy Regularization for Autonomous Gait Transition and Energy-Efficient Quadruped Locomotion
Boyuan Liang, Lingfeng Sun, Xinghao Zhu +6
In reinforcement learning for legged robot locomotion, crafting effective reward strategies is crucial. Pre-defined gait patterns and complex reward systems are widely used to stab…