198 citations · 203 across the 4 of their papers we have counts for
4 papers
Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations
Xiaofeng Mao, Yucheng Xu, Ruoshi Wen +5
Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-effic…
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…
Accessibility-Based Clustering for Efficient Learning of Locomotion Skills
Chong Zhang, Wanming Yu, Zhibin Li
For model-free deep reinforcement learning of quadruped locomotion, the initialization of robot configurations is crucial for data efficiency and robustness. This work focuses on a…
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…