Despite vastly slower "hardware" and "wetware," human dexterity vastly out-performs modern robots. This project studies apparently-simple tasks - managing the kinematic constraint on hand motion required to open a door; and dealing with the dynamic complexity of liquid sloshing in a cup of coffee - that profoundly challenge robots but humans perform with ease. The key idea is that humans manage skillful physical interaction with these objects by exploiting clever combinations of primitive dynamic actions that do not require continuous intervention. A novel theory to describe the effectiveness of this approach is developed and tested by experiments with human subjects. The theory is applied to transfer comparable skill to robots, despite manifestly different hardware. If successful, these robots will be more capable, more comprehensible, and more collaborative partners with humans.
The central experimental challenge is to determine the essential strategy underlying humans? remarkable competence in physical interaction tasks. Three hypotheses reflecting major themes in contemporary motor neuroscience are tested: Humans 1) develop models of object dynamics sufficient to pre-compute and execute required hand motions (similar to modern robot programming); 2) choose forces and motions to minimize muscular effort (similar to optimizing efficiency); or 3) exploit dynamic primitives to robustly achieve satisficing (good-enough) performance. The theoretical challenge is to formulate a coherent account combining the information-processing of brains (or computers) with the "energy-processing" of physical objects and their interactions. Classical equivalent circuit theory is re-purposed to define a neo-classical equivalent network theory, combining dynamic motion primitives with mechanical impedances (interactive dynamics). Mechanical impedances enjoy a key property, compositionality, that overcomes the curse of dimensionality.
