(Georgia Institute of Technology, 2008-05)
Stilman, Mike; Nishiwaki, Koichi; Kagami, Satoshi
We present results of successful telemanipulation
of large, heavy objects by a humanoid robot. Using a single
joystick the operator controls walking and whole body
manipulation along arbitrary paths for up to ten minutes of
continuous execution. The robot grasps, walks, pushes, pulls,
turns and re-grasps a 55kg range of loads on casters. Our
telemanipulation framework changes reference frames online
to let the operator steer the robot in free walking, its hands
in grasping and the object during mobile manipulation. In the
case of manipulation, our system computes a robot motion that
satisfies the commanded object path as well as the kinematic
and dynamic constraints of the robot. Furthermore, we achieve
increased robot stability by learning dynamic friction models
of manipulated objects.
(Georgia Institute of Technology, 2007-11)
Stilman, Mike; Nishiwaki, Koichi; Kagami, Satoshi
We present a successful implementation of rigid
grasp manipulation for large objects moved along specified
trajectories by a humanoid robot. HRP-2 manipulates tables on
casters with a range of loads up to its own mass. The robot
maintains dynamic balance by controlling its center of gravity
to compensate for reflected forces. To achieve high performance
for large objects with unspecified dynamics the robot learns a
friction model for each object and applies it to torso trajectory
generation. We empirically compare this method to a purely
reactive strategy and show a significant increase in predictive
power and stability.
(Georgia Institute of Technology, 2007)
Stilman, Mike; Nishiwaki, Koichi; Kagami, Satoshi; Kuffner, James J.
This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain
of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of
an environment filled with various fixed and movable obstacles, and automatically plans a navigation
strategy to reach a desired goal location. The planned strategy consists of a sequence of walking
and compliant manipulation operations. It is executed by the robot with online feedback. We give
an overview of our NAMO system, as well as provide details of the autonomous planning, online
grasping and compliant hand positioning during dynamically-stable walking. Finally, we present
results of a successful implementation running on the Humanoid Robot HRP-2.