Prophetic Goal-Space Planning for Human-in-the-Loop Mobile Manipulation

Joshua James, Yifan Weng, Stephen Hart, Patrick Beeson, and Robert Burridge. Prophetic Goal-Space Planning for Human-in-the-Loop Mobile Manipulation. In Proceedings of the IEEE RAS Humanoids Conference, Seoul, Korea, November 2015.

Abstract

This paper introduces the Prophetic Goal-Space Planner (PGP) system developed to accomplish mobile manipulation tasks. Goal-space planning exploits the under-constrained and variable nature of many real-world tasks by defining Cartesian goals in terms of intuitive solution manifolds (e.g., the principal axis of a cylindrical handle, the rim of a valve, the surface of a step) rather than precise points in 6-dimensional space. The PGP system combines 1) a goal-space planner, 2) a kinematic planner and visualizer called the prophet, and 3) user interface tools. The prophet determines appropriate stance locations for the robot to reach its goals and animates the result. The UI tools allow a human operator to either approve or reject the planner's output (the "prophecy"), request new plans, or adjust the goal-space target regions to better match the robot's perceived environment. The PGP system thus enables a level of shared autonomy between the robot and operator that can ensure task completion with only limited, corrective operator input. PGP was employed by Team TRACLabs on the Boston Dynamics Atlas robot for the DARPA Robotics Challenge (DRC) 2015 Finals. PGP enabled the efficient completion of multiple manipulation tasks, including opening and walking through a door, walking up to and turning a valve, and throwing a lever---despite degraded network communication channels---and contributed to TRACLabs' placing 9th out of 23 teams in the competition.

Additional Information

Selected for oral presentation

BibTeX

@InProceedings{James-humanoids-15,
  author =       {Joshua James and Yifan Weng and Stephen Hart and Patrick
                  Beeson and Robert Burridge},
  title =        {Prophetic Goal-Space Planning for Human-in-the-Loop Mobile
                  Manipulation},
  booktitle =    {Proceedings of the IEEE RAS Humanoids Conference},
  year =         2015,
  address =      {Seoul, Korea},
  month =        {November},
  abstract =     {This paper introduces the Prophetic Goal-Space Planner (PGP)
                  system developed to accomplish mobile manipulation
                  tasks. Goal-space planning exploits the under-constrained
                  and variable nature of many real-world tasks by defining
                  Cartesian goals in terms of intuitive solution manifolds
                  (e.g., the principal axis of a cylindrical handle, the rim
                  of a valve, the surface of a step) rather than precise
                  points in 6-dimensional space. The PGP system combines 1) a
                  goal-space planner, 2) a kinematic planner and visualizer
                  called the prophet, and 3) user interface tools. The prophet
                  determines appropriate stance locations for the robot to
                  reach its goals and animates the result. The UI tools allow
                  a human operator to either approve or reject the planner's
                  output (the "prophecy"), request new plans, or adjust the
                  goal-space target regions to better match the robot's
                  perceived environment. The PGP system thus enables a level
                  of shared autonomy between the robot and operator that can
                  ensure task completion with only limited, corrective
                  operator input. PGP was employed by Team TRACLabs on the
                  Boston Dynamics Atlas robot for the DARPA Robotics Challenge
                  (DRC) 2015 Finals. PGP enabled the efficient completion of
                  multiple manipulation tasks, including opening and walking
                  through a door, walking up to and turning a valve, and
                  throwing a lever---despite degraded network communication
                  channels---and contributed to TRACLabs' placing 9th out of
                  23 teams in the competition.},
  bib2html_pubtype ={Refereed Conference},
  bib2html_rescat ={Humanoids},
  bib2html_extra_info ={Selected for oral presentation}
}

Download:

[2.3MB pdf]