2 citations · 2 across the 6 of their papers we have counts for
6 papers
PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
Zhengmao He, Moonkyu Jung, Hyeongjun Kim +4
Loco-manipulation has recently shown promising capabilities; however, achieving high-precision control, managing the high-dimensional action space induced by many degrees of freedo…
Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation
Zhe Zhao, Zhibin Li, Yilin Ou +1
Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware due…
Learning Dynamic Pick-and-Place for a Legged Manipulator
Moonkyu Jung, Jiseong Lee, Zhengmao He +8
Legged manipulators extend robotic capabilities beyond static manipulation by integrating agile locomotion with versatile arm control. However, achieving precise manipulation while…
Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation
Zhe Zhao, Haoyu Dong, Zhengmao He +3
Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains diffic…
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
Zhengmao He, Kun Lei, Yanjie Ze +3
Quadruped robots are progressively being integrated into human environments. Despite the growing locomotion capabilities of quadrupedal robots, their interaction with objects in re…
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Kun Lei, Zhengmao He, Chenhao Lu +3
Combining offline and online reinforcement learning (RL) is crucial for efficient and safe learning. However, previous approaches treat offline and online learning as separate proc…