Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy resolution methods often ignore task-specific force demands or maximize the force capability indiscriminately, sacrificing dexterity when l...
Xiao Wang, Heng Zhang, Gokhan Solak et al.· 0 citations
PA-RL, a reinforcement-learning framework that uses artificial potential fields as the action representation, is proposed and is the only method to reach a 100% evaluation success rate within the allotted training time, while the best baseline reaches 92.6%.
Experimental results show that LUCID improves the full-task success and partial-completion rates compared to prior baseline methods, demonstrating its effectiveness in complex sequential loco-manipulation tasks.
Cheng Guo, Mingzhe Ni, A. Cangelosi et al.· 0 citations
A unified framework that combines centralized training with decentralized execution (CTDE) and a Hybrid Reward Architecture (HRA) is introduced that enables multiple actors to share a centralized multi-head critic and substantially improves both sample efficiency and policy performance.
Changhao Li, Yifang Zhang, Heng Zhang et al.· 0 citations
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