Object Manipulation

Object manipulation research focuses on enabling robots and AI agents to interact with and move objects effectively, often in complex or unpredictable environments. Current efforts concentrate on improving model-based control, leveraging diverse sensor modalities (vision, tactile feedback), and employing advanced algorithms like reinforcement learning, diffusion models, and optimal transport for tasks such as grasping, reorientation, and precise placement. These advancements are crucial for robotics, particularly in areas like warehouse automation, assistive technologies, and underwater intervention, where robust and adaptable object manipulation is essential. Furthermore, research explores how to improve the efficiency of learning through techniques like curriculum learning and semantic exploration.