The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present an approach to motion planning for highly articulated systems that aims to ensure robust execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By simulating the robot's perception system during search, our planner generates a metric, the so-called perceptive capability, that quantifies the `sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous manipulations as performed by a humanoid robot in a kitchen environment. Our results indicate that reasoning about the future perceptive capability has the potential to greatly facilitate any task requiring visual feedback during control of the robot manipulator and can thus ensure higher task success rates than perception-unaware planning.

Keywords

Humanoid Robots, Perception, Motion Planning, Manipulation Planning

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Perception for Humanoid Robots

Number of pages: 6

Text Reference

Philipp Michel, Christian Scheurer, James Kuffner, Nikolaus Vahrenkamp, and R?iger Dillmann, "Planning for Robust Execution of Humanoid Motions using Future Perceptive Capability," Proceedings of the IEEE/RSJ IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07), October, 2007, pp. 3223-3228.

BibTeX Reference

@inproceedings{Michel_2007_6004,
   author = "Philipp Michel and Christian Scheurer and James Kuffner and Nikolaus Vahrenkamp and R?iger Dillmann",
   title = "Planning for Robust Execution of Humanoid Motions using Future Perceptive Capability",
   booktitle = "Proceedings of the IEEE/RSJ IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07)",
   pages = "3223-3228",
   month = "October",
   year = "2007",
}

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