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Using the context of human-supervised object collection tasks, we explore policies for a robot to seek assistance from a human supervisor and avoid loss of human trust in the robot. We consider a human-robot interaction scenario in which a mobile manipulator chooses to collect objects either autonomously or through human assistance; while the human supervisor monitors the robot’s operation, assists when asked, or intervenes if the human perceives that the robot may not accomplish its goal. We design an optimal assistance-seeking policy for the robot using a Partially Observable Markov Decision Process (POMDP) setting in which human trust is a hidden state and the objective is to maximize collaborative performance. We conduct two sets of human-robot interaction experiments. The data from the first set of experiments is used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy that is used in the second experiment. For most participants, the estimated POMDP reveals that humans are more likely to intervene when their trust is low and the robot is performing a high-complexity task; and that the robot asking for assistance in high-complexity tasks can increase human trust in the robot. Our experimental results show that the proposed trust-aware policy yields superior performance compared with an optimal trust-agnostic policy.
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Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement
Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the object or ask for human assistance. The human supervisor also has the choice to rely on or interrupt the robot. Using data from initial experiments, we model the dynamics of human trust and engagement using a linear dynamical system (LDS). Furthermore, we develop a human action model to define the probability of human reliance on the robot. Our model suggests that participants are more likely to interrupt the robot when their trust and engagement are low during high-complexity collection tasks. Using Model Predictive Control (MPC), we design an optimal assistance-seeking policy. Evaluation experiments demonstrate the superior performance of the MPC policy over the baseline policy for most participants.
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- Award ID(s):
- 2024649
- PAR ID:
- 10616860
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7502-2
- Page Range / eLocation ID:
- 951 to 956
- Format(s):
- Medium: X
- Location:
- Pasadena, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RO-MAN60168.2024.10731300
