Robot Control

Robot control research aims to develop algorithms and architectures enabling robots to perform complex tasks autonomously and safely. Current efforts focus on improving robustness to uncertainty, using deep reinforcement learning (often with model architectures like Deep Q Networks and diffusion models), and incorporating human-in-the-loop control strategies for enhanced safety and efficiency. These advancements are crucial for deploying robots in diverse real-world settings, impacting fields ranging from industrial automation and assistive robotics to warehouse logistics and human-robot collaboration.