198 citations · 225 across the 5 of their papers we have counts for
8 papers · 1 filter
Discovery of skill switching criteria for learning agile quadruped locomotion
Wanming Yu, Fernando Acero, Vassil Atanassov +4
This paper develops a hierarchical learning and optimization framework that can learn and achieve well-coordinated multi-skill locomotion. The learned multi-skill policy can switch…
Identifying Important Sensory Feedback for Learning Locomotion Skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy +5
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there…
Multi-expert learning of adaptive legged locomotion
Chuanyu Yang, Kai Yuan, Qiuguo Zhu +2
Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to ge…
Learning natural locomotion behaviors for humanoid robots using human knowledge
Chuanyu Yang, Kai Yuan, Shuai Heng +2
This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomoti…
Reaching, Grasping and Re-grasping: Learning Multimode Grasping Skills
Wenbin Hu, Chuanyu Yang, Kai Yuan +1
The ability to adapt to uncertainties, recover from failures, and coordinate between hand and fingers are essential sensorimotor skills for fully autonomous robotic grasping. In th…
Learning Pregrasp Manipulation of Objects from Ungraspable Poses
Zhaole Sun, Kai Yuan, Wenbin Hu +2
In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped fro…