(Georgia Institute of Technology, 2011-05)
Kunz, Tobias; Kingston, Peter; Stilman, Mike; Egerstedt, Magnus B.
We introduce and experimentally validate a novel
algorithmic model for physical human-robot interaction with
hybrid dynamics. Our computational solutions are complementary
to passive and compliant hardware. We focus on the case
where human motion can be predicted. In these cases, the
robot can select optimal motions in response to human actions
and maximize safety. By representing the domain as a Markov
Game, we enable the robot to not only react to the human but
also to construct an infinite horizon optimal policy of actions
and responses. Experimentally, we apply our model to simulated
robot sword defense. Our approach enables a simulated 7-DOF
robot arm to block known attacks in any sequence. We generate
optimized blocks and apply game theoretic tools to choose the
best action for the defender in the presence of an intelligent
adversary.