Compliance Controller

Compliance controllers are algorithms that enable robots to interact safely and effectively with their environments, particularly during physical tasks like assembly or human-robot interaction. Current research focuses on improving controller adaptability using techniques like reinforcement learning and model-based methods to handle variations in object geometry or human behavior, often incorporating force feedback and contextual information. These advancements are crucial for enhancing the robustness and reliability of robots in various applications, from industrial automation to assistive technologies, by enabling safer and more intuitive interactions. Furthermore, research is exploring the ethical implications of compliance controllers, particularly in social robotics, by developing methods to manage privacy and build trust in human-robot interactions.