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Human-Robot Interaction
Machine Learning with Application to Robotics
Fast Adaptive Control
Dexterous Manipulation and Grasping
Computational Neuroscience and Cognitive Modeling

Dexterous manipulation and grasping

The human hand has incredible functionality. Our research focuses on understanding and replicating this capacity to robotic hands with very different kinematics.


We develop approaches and applications:
  • grasp planning: generate a variety of task-oriented grasps with hands by leveraging various learning strategies e.g., off-line optimization, learning from human demonstration
  • stable grasping: adapt the necessary force on-line to an object to achieve a safe grasp
  • dexterous manipulation: able to complete tasks with complicated dexterous manipulation
  • object interaction: learning an object manipulation strategy from human demonstration
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