Dexterous Robotic Manipulation
Dexterous robotic manipulation focuses on enabling robots to perform complex, human-like interactions with objects, requiring precise control and adaptability. Current research emphasizes learning-based approaches, particularly reinforcement learning and imitation learning, often employing neural networks like deep deterministic policy gradients and diffusion models, to acquire manipulation skills from both simulated and real-world data. These advancements are crucial for expanding robotic capabilities in diverse applications, such as household tasks and industrial settings, by improving the robustness and efficiency of robot control in unstructured environments. The integration of tactile sensing and physics-based modeling further enhances the accuracy and adaptability of these systems.