117 citations · 327 across the 26 of their papers we have counts for
7 papers · 1 filter
GFPose: Learning 3D Human Pose Prior with Gradient Fields
Hai Ci, Mingdong Wu, Wentao Zhu +4
Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the co…
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao +6
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible d…
End-to-End Affordance Learning for Robotic Manipulation
Yiran Geng, Boshi An, Haoran Geng +3
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can…
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
Mingdong Wu, Fangwei Zhong, Yulong Xia +1
Object Rearrangement is to move objects from an initial state to a goal state. Here, we focus on a more practical setting in object rearrangement, i.e., rearranging objects from sh…
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
Yan Zhao, Ruihai Wu, Zhehuan Chen +4
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that…
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang +8
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through re…
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
Tianhao Wu, Fangwei Zhong, Yiran Geng +4
Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined obje…