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Massachusetts Institute of Technology

Imitation learning of whole-body grasps

Abstract

dc:description.abstract

Humans often learn to manipulate objects by observing other people. In much the same way, robots can use imitation learning to pick up useful skills. A system is demonstrated here for using imitation learning to teach a robot to grasp objects using both hand and whole-body grasps, which use the arms and torso as well as hands. Demonstration grasp trajectories are created by teleoperating a simulated robot to pick up simulated objects, and stored as sequences of keyframes in which contacts with the object are gained or lost. When presented with a new object, the system compares it against the objects in a stored database to pick a demonstrated grasp used on a similar object. Both objects are modeled as a combination of primitives-boxes, cylinders, and spheres-and the primitives for each object are grouped into 'functional groups' that geometrically match parts of the new object with similar parts of the demonstration object. These functional groups are then used to map contact points from the demonstration object to the new object, and the resulting adapted keyframes are adjusted and checked for feasibility. Finally, a trajectory is found that moves among the keyframes in the adapted grasp sequence, and the full trajectory is tested for feasibility by executing it in the simulation. The system successfully uses this method to pick up 92 out of 100 randomly generated test objects in simulation.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2005

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Hsiao, Kaijen
Advisor dc:contributor.advisor
  • Tomász Lozano-Pérez.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/33761
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/33761

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Hsiao, Kaijen. Imitation learning of whole-body grasps. Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33761