TY - GEN
T1 - Proactive Opinion-Driven Robot Navigation Around Human Movers
AU - Cathcart, Charlotte
AU - Santos, Maria
AU - Park, Shinkyu
AU - Leonard, Naomi Ehrich
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's 'opinion' for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.
AB - We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's 'opinion' for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.
UR - http://www.scopus.com/inward/record.url?scp=85180357315&partnerID=8YFLogxK
U2 - 10.1109/IROS55552.2023.10341745
DO - 10.1109/IROS55552.2023.10341745
M3 - Conference contribution
AN - SCOPUS:85180357315
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4052
EP - 4058
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Y2 - 1 October 2023 through 5 October 2023
ER -