Computational Motor Control, Humanoid Robotics, and their Societal Relevance
Computational motor control is a research field that attempts to understand the neural control of primate movement in terms of mathematical theories of action and perception. Humanoid robotics aims at creating a new kind of robotic systems that can share human living space and infrastructure, with the goal to entertain and assist humans in their daily lives. Many research goals are shared between these two fields.
This talk will describe our work towards understanding human and humanoid
motor skill generation and learning using an interdisciplinary approach.
At the highest level, we are interested in interactive skill acquisition,
for instance using imitation learning. At a lower level, we need to address
the basic principles of decomposing human movement into smaller motor primitives,
and how to learn such primitives and sequencing of primitives. At the lowest
level, dexterous motor control and motor learning needs to be addressed
in order to understand the compliant and fault tolerant strategies of human
movement and their realization in robotics.
We have accompanied technical research in these areas with behavioral and neuro-imaging studies, and realized our results in humanoid robotics implementation, where a humanoid robot accomplished various complex manipulation and imitation skills. We will also outline how this line of research has important implications for the new wave of societally relevant assistive robotics, e.g., as needed in elder care, robot rehabilitation and physical therapy, intelligent prosthetics, etc.