Robot Behavior

Robot behavior research focuses on designing and improving robots' actions and interactions, aiming for safe, efficient, and human-compatible performance. Current research emphasizes learning from diverse data sources (internet data, human demonstrations, sensor feedback) to enable robots to acquire complex skills, predict human intentions, and personalize their behavior based on user preferences. This involves utilizing various model architectures, including deep learning models, large language models, and reinforcement learning algorithms, to achieve these objectives. The field's advancements are crucial for developing robots capable of seamlessly collaborating with humans in various settings, from industrial automation to assistive technologies.