The Robotics Institute

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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

 

Time and Place

Tuesday, April 5, 2011
NSH 3305
Talk 4:30 pm

Abstract

 

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.

 

Bio

 

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.