Robotic Scenario

Robotic scenario research focuses on developing robust and safe control architectures for robots operating in diverse and unpredictable environments, addressing challenges like visual perception in challenging conditions (e.g., underwater or during surgery) and safe interaction with humans. Current research employs methods such as model predictive control with control barrier functions for safety guarantees, deep reinforcement learning for policy optimization in complex tasks, and integration of language models for high-level planning and human-robot interaction. These advancements aim to improve robot performance in various applications, from minimally invasive surgery and assistive robotics to autonomous navigation and chemical manipulation, ultimately leading to more reliable and adaptable robotic systems.