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Team-centric motion planning in unfamiliar environments (Conference Presentation)
Author(s): Cory Hayes; Matthew Marge; Claire Bonial; Clare Voss; Susan G. Hill

Paper Abstract

Technological advances in artificial intelligence have created an opportunity for effective teaming between humans and robots. Reliable robot teammates could enable increased situational awareness and reduce the cognitive burden on their human counterparts. Robots must operate in ways that follow human expectations for effective teaming, whether operating near their human teammates or at a distance and out of sight. This ability would allow people to better anticipate robot behavior after issuing commands. In comparison to traditional human-agnostic and proximal human-aware path planning, our work addresses a relatively unexplored third area, team-centric motion planning: robots navigating remotely in an unfamiliar area and in a way that meets a teammate's expectations. In this paper, we discuss initial work towards encoding human intention to inform autonomous robot navigation. Our approach leverages the methodology and data collected in an ongoing series of natural dialogue experiments where naive participants provide navigation instructions to a remote robot situated in an unfamiliar environment. Participants are tasked with uncovering specific information about the environment via the remote robot through real-time mapping and snapshots, requiring fine- grained robot movement that meets the intention of a given command. This sensitivity often leads to clarification commands to augment the position and orientation of the robot in order to achieve the desired instructor intention; we seek to reduce or eliminate the need for these clarification commands for more efficient task completion. Our current efforts use known participant responses to executed commands to train a reinforcement learning policy for building awareness about unknown environments. Ultimately, this approach would lead to robot movement that maximizes the amount of relevant information relayed back to a human instructor while minimizing instructor burden.

Paper Details

Date Published: 14 May 2018
PDF
Proc. SPIE 10642, Degraded Environments: Sensing, Processing, and Display 2018, 106420K (14 May 2018); doi: 10.1117/12.2309414
Show Author Affiliations
Cory Hayes, U.S. Army Research Lab. (United States)
Matthew Marge, U.S. Army Research Lab. (United States)
Claire Bonial, U.S. Army Research Lab. (United States)
Clare Voss, U.S. Army Research Lab. (United States)
Susan G. Hill, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 10642:
Degraded Environments: Sensing, Processing, and Display 2018
John (Jack) N. Sanders-Reed; Jarvis (Trey) J. Arthur, Editor(s)

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