L Yuan, Z Zhang, L Li, C Guan, Y Yu - arXiv preprint arXiv:2312.01058, 2023 - arxiv.org
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and has made progress in various fields. Specifically, cooperative MARL focuses on training a …
When robots interact with humans in homes, roads, or factories the human's behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners …
When robots interact with human partners, often these partners change their behavior in response to the robot. On the one hand this is challenging because the robot must learn to …
S Habibian, DP Losey - IEEE Robotics and Automation Letters, 2022 - ieeexplore.ieee.org
Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how …
S Sagheb, YJ Mun, N Ahmadian… - … on Robotics and …, 2023 - ieeexplore.ieee.org
When humans interact with robots influence is inevitable. Consider an autonomous car driving near a human: the speed and steering of the autonomous car will affect how the …
Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do …
This paper introduces an innovative approach to addressing leader-follower control challenges through iterative learning techniques. In this scheme, both the leader and the …
OG Kio, M Yuan, RS Allison… - 2024 IEEE 4th …, 2024 - ieeexplore.ieee.org
The likelihood of an agent's success in achieving its goals and subgoals under specific operational constraints is an important factor in performance-based trust. Trust management …
The growing presence of modern learning robots necessitates a fundamental shift in design, as these robots must learn skills from human inputs. Two main components close the loop in …