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Found 6 papers, 5 papers with code
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly.
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games
Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation.
Diffusion Models for Multi-target Adversarial Tracking
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited.
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent.
Learning Models of Adversarial Agent Behavior under Partial Observability
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.
Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive.
