DEXMAN

   Roboticists have made huge efforts to mimic the human hand, not only from the form but also from the functionalities. However, robustly grasping and manipulating an unknown object/tool is still an open question to be thoroughly solved. In this project, we will investigate a human motor skill extraction based approach to achieve robust dexterous grasping and in-hand manipulation on a robotic arm/hand system:   

• A novel framework of augmented dynamic movement primitives DMPs embedding perception information for human skill extraction and generalization to new tasks
• Reconstructing and tracking an unknown object by exploiting interactive manipulation and multi-modal feedback
• Multiple sensor fusion based adaptive grasping and manipulation control framework enhanced by human motor skills extraction

    This is a joint project sponsored by DFG in the context of sino-german joint research. Three institutes with a clear record in human motor skills learning (SCUT), visuo-tactile based recognition and interaction (UNIBI), and visuo-tactile based adaptive grasping and dexterous manipulation with multi-fingered robotic hands (DLR), will tightly cooperate towards this aim.