Modeling Collision Avoidance Decisions by a Simulated Human-Autonomy Team
Abstract
Creating autonomous teammates that respond to their human counterparts in an adaptive way requires models of human goal and intent. One approach to building such models is inverse reinforcement learning, a process by which a reward function, representing goals and intent in a task, is learned from observed behavior. Here, we validate an approach to inverse reinforcement learning in a collision avoidance task completed by a simulated human-autonomy team. Our findings indicate several promising aspects of our approach, as well as some clear drawbacks for future work to investigate.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2023
- Accession Number
- AD1190464
Entities
People
- Evan C. Carter
- Vernon J. Lawhern