Coordinated Motion

Coordinated motion research focuses on enabling multiple agents, ranging from robots to vehicles, to move together efficiently and effectively, often under constraints like limited resources or adversarial environments. Current efforts leverage diverse approaches, including reinforcement learning with novel architectures like subequivariant networks and optimization techniques for task assignment and trajectory planning, to achieve robust and adaptable coordination. This field is crucial for advancing robotics, autonomous systems, and human-robot interaction, with applications spanning collaborative manipulation, swarm robotics, and human-assisted rehabilitation. The development of efficient and generalizable coordination strategies is key to unlocking the full potential of multi-agent systems.