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Integrating human observer inferences into robot motion planning


Our goal is to enable robots to produce motion that is suitable for human–robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.

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We thank Geoff Gordon, Jodi Forlizzi, Hendrik Christiansen, Kenton Lee, Chris Dellin, Alberto Rodriguez, and the members of the Personal Robotics Lab for fruitful discussion and advice. This material is based upon work supported by NSF-IIS-0916557, NSF-EEC-0540865, ONR-YIP 2012, the Intel Embedded Computing ISTC, and the Intel Ph.D. Fellowship.

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Correspondence to Anca Dragan.

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This paper combines work from Dragan et al. (2013), and Dragan and Srinivasa (2013).

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Dragan, A., Srinivasa, S. Integrating human observer inferences into robot motion planning. Auton Robot 37, 351–368 (2014).

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  • Human–robot collaboration
  • Predictability
  • Legibility
  • Trajectory optimization
  • Action interpretation