Randomized multi-modal motion planning for a humanoid robot manipulation task

Published

Journal Article

Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC. © 2011 The Author(s).

Full Text

Duke Authors

Cited Authors

  • Hauser, K; Ng-Thow-Hing, V

Published Date

  • May 1, 2011

Published In

Volume / Issue

  • 30 / 6

Start / End Page

  • 678 - 698

Electronic International Standard Serial Number (EISSN)

  • 1741-3176

International Standard Serial Number (ISSN)

  • 0278-3649

Digital Object Identifier (DOI)

  • 10.1177/0278364910386985

Citation Source

  • Scopus