(Georgia Institute of Technology, 2014)
Levihn, Martin; Nishiwaki, Koichi; Kagami, Satoshi; Stilman, Mike
Legged robots have unique capabilities to traverse
complex environments by stepping over and onto objects. Many
footstep planners have been developed to take advantage of
these capabilities. However, legged robots also have inherent
constraints such as a maximum step height and distance. These
constraints typically limit their reachable space, independent
of footstep planning. Thus, we propose that robots such as
humanoid robots that have manipulation capabilities should use
them. A robot should autonomously modify its environment if
necessary. We present a system that enabled a real robot to use
a box to create itself a stair step or place a board on the ground
to cross a gap, allowing it to reach its otherwise unreachable
goal configuration.
(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.