DEXMAN: Improving robot’s DEXterous MANipulability by learning stiffness-based human motor skills and visuo-tactile exploration

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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 extractionTo represent human motor skills while interacting with objects/tools that differ in size, shape and stiffness, we will create an augmented hierarchical primitive-based library, with respect to human hand/arm stiffness, motion and finger gaiting. With online perception feedback, this primitives library will provide the knowledge basis for generalizable skills transferring from human to a robotic hand-arm system, by a hierarchical adaptive grasping and manipulation control method based on multi-modal sensor fusion. The skills generalization will be achieved regarding different object/tool sizes, shapes and stiffness. Object properties will be modelled online through visuo/tactile based exploration control method. Multi-modal perception feedback will also be used as input of the primitive library to generalize motion, stiffness, and gaiting trajectories. We will demonstrate the proposed grasping and manipulation approach with a typical daily-of-live task such as grasping a knife and cutting a fruit. 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.