Force Control

Force control in robotics aims to enable robots to interact with their environment by precisely regulating forces and torques during manipulation and locomotion tasks. Current research heavily emphasizes learning-based approaches, incorporating techniques like deep reinforcement learning (e.g., PPO, DRL), imitation learning, and neural networks (e.g., CNNs, LSTMs) to improve adaptability and robustness in diverse scenarios, including contact-rich manipulation and human-robot collaboration. These advancements are crucial for enhancing the safety and dexterity of robots in various applications, from manufacturing and surgery to assistive technologies and exploration. The development of accurate force sensing and control algorithms is driving progress towards more versatile and reliable robots capable of performing complex tasks in unstructured environments.