198 citations · 324 across the 11 of their papers we have counts for
11 papers
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…
Learning Quadruped Locomotion using Bio-Inspired Neural Networks with Intrinsic Rhythmicity
Chuanyu Yang, Can Pu, Tianqi Wei +2
Biological studies reveal that neural circuits located at the spinal cord called central pattern generator (CPG) oscillates and generates rhythmic signals, which are the underlying…
A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments
Can Pu, Chuanyu Yang, Jinnian Pu +1
To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator…
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…
Learning Whole-body Motor Skills for Humanoids
Chuanyu Yang, Kai Yuan, Wolfgang Merkt +3
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, fo…
Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning
Chuanyu Yang, Taku Komura, Zhibin Li
This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. Conventional zero moment point b…
Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge
Doo Re Song, Chuanyu Yang, Christopher McGreavy +1
This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy G…