Reactive Behavior

Reactive behavior research focuses on designing systems that dynamically adapt to changing environments and unexpected events, primarily aiming to improve efficiency and robustness. Current research emphasizes the development of novel algorithms and architectures, such as reinforcement learning, behavior trees, and neural networks (including implicit neural representations), to enable reactive control in diverse applications like autonomous driving, robotics, and human-robot interaction. This work is significant for advancing the capabilities of autonomous systems across various domains, improving safety, and enabling more natural and intuitive interactions between humans and machines.