Robot Manipulation

Robot manipulation research focuses on enabling robots to dexterously interact with their environment, primarily through the development of robust and generalizable control policies. Current efforts concentrate on improving learning efficiency via techniques like reinforcement learning (RL) combined with large language models (LLMs) for task decomposition and feedback, and leveraging advanced simulation methods (e.g., Gaussian splatting, model reduction) to bridge the sim-to-real gap. These advancements are crucial for expanding the capabilities of robots in diverse applications, from industrial automation to assistive technologies and home robotics.