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


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