Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

Abstract

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

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Notes

  1. 1.

    Note that \(\ell (\bar{\mathbf {x}},\bar{\mathbf {u}},t)\) is convex since \(\mathbf {R} \succ 0\).

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Acknowledgments

We gratefully acknowledge the support of the Defense Advanced Research Projects Agency via Air Force Research Laboratory award FA8750-12-1-0321, the Office of Naval Research via awards N00014-12-1-0071 and N00014-10-1-0951, NSF awards IIS-0746194 and IIS-1161909, MIT, and MIT CSAIL. Robin Deits is supported by the Fannie and John Hertz Foundation. We are also grateful to Boston Dynamics and Carnegie Robotics for their support during the DRC. We would like to thank the members of the Robot Locomotion Group and the MIT DRC Team for their insights and supporting contributions to this work. A special thanks to Matt Antone for developing LIDAR calibration and terrain map software that our experiments relied upon.

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Correspondence to Scott Kuindersma.

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Kuindersma, S., Deits, R., Fallon, M. et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Auton Robot 40, 429–455 (2016). https://doi.org/10.1007/s10514-015-9479-3

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Keywords

  • Humanoid
  • Legged locomotion
  • Optimization
  • State estimation