4 papers
Stage-Transition Dense Reward Modeling for Reinforcement Learning
Yang Yang, Bingjie Chen, Zihan Wang +4
Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to…
HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms
Bingjie Chen, Zihan Wang, Zhe Han +3
Traditional IK methods for redundant humanoid manipulators emphasize end-effector (EE) tracking, frequently producing configurations that are valid mechanically but not human-like.…
CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
Bingjie Chen, Keyu Fan, Qi Yang +6
Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework…
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
Bingjie Chen, Yancong Wei, Rihao Liu +5
Whole-body reactive obstacle avoidance for mobile manipulators (MM) remains an open research problem. Control Barrier Functions (CBF), combined with Quadratic Programming (QP), hav…