(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.