Exploring how learning systems can adapt, coordinate, and move from simulation into the real world.
I work at the intersection of reinforcement learning, multi-agent systems, robotics, and control.
My most recent published work is ADiCo, a framework that combines multi-agent reinforcement learning with Extremum Seeking Control to automatically adapt behavioral diversity based on task performance.
My current work explores how these ideas can be applied to robotic systems, sim-to-real learning, and autonomous multi-agent decision-making.
Multi-Agent Reinforcement Learning 路 Robotics 路 Adaptive Control
Robot Learning 路 Sim-to-Real 路 Autonomous Systems
Strategic AI 路 Emergent Multi-Agent Behavior
馃敩 ADiCoAdaptive behavioral diversity in cooperative multi-agent reinforcement learning using Extremum Seeking Control. |
Taking learned multi-agent policies from simulation toward ROS2 and robotic systems. |
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Learning robotic manipulation trajectories from demonstrations. |
馃幃 StarCraft.aiExploring strategic decision-making and skill transfer in StarCraft II. |
