Foundations of Robotics Seminar, April 5, 2011
Time
and Place | Seminar Abstract
Robust Grasping Under Uncertainty in Pose and/or Shape
Kaijen Hsiao
Research Team
Willow Garage
Tuesday, April 5, 2011
NSH 3305
Talk 4:30 pm
Robotic manipulation of objects is much more difficult in unstructured
environments (such as peoples' homes) than in structured ones (such as
factories) because of the presence of uncertainty. Uncertainty comes
in many forms in manipulation tasks: uncertainty in object identities,
poses, or shapes, or in robot poses or shapes, for instance.
In this talk, I will discuss my thesis work on grasping objects of known
shape robustly under significant uncertainty in object pose. To reason
explicitly about uncertainty while grasping, we model the problem as a
partially observable Markov decision process (POMDP). We derive a
closed-loop strategy that maintains a belief state (a probability
distribution over world states), and select actions with a receding
horizon using forward search through the belief space. Our actions
are world-relative trajectories (WRT): fixed trajectories expressed
relative to the most-likely state of the world. We localize the
object, ensure its reachability, and robustly grasp it at a specified
position by using information-gathering, reorientation, and
goal-seeking WRT actions. This framework is used to grasp objects
(including a power drill and a Brita pitcher) despite significant pose
uncertainty, using a 7-DOF Barrett Arm and attached 4-DOF Barrett Hand
equipped with force and contact sensors.
I will also present more recent work on reactively adjusting
grasps in a model-free way using tactile sensors during grasp execution,
as well as work on selecting grasps under uncertainty in object shape, in
which we probabilistically combine results from multiple grasp
planners/evaluators on multiple potential object representations to select
grasps that are most likely to work given all the possible object hypotheses.
Both are demonstrated using the PR2 robot from Willow Garage.
Kaijen Hsiao received her B.S.E. degree in Mechanical Engineering in
2002 from Princeton University and her Ph.D. degree in Computer
Science in 2009 from MIT, where she worked with Tomas Lozano-Perez and
Leslie Kaelbling. She is now a research scientist at Willow Garage,
where her research interests lie in grasping and manipulation.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.