Compliance Control

Compliance control in robotics aims to enable robots to interact safely and effectively with unpredictable environments by adapting their behavior to contact forces. Current research focuses on developing robust and adaptable compliance control strategies using methods like deep reinforcement learning, hierarchical quadratic programming, and learning from demonstrations, often incorporating force/torque sensing and advanced model architectures such as transformers and LSTMs. These advancements are crucial for enabling robots to perform complex tasks like assembly, manipulation, and human-robot collaboration, improving safety and efficiency in various applications.