Manipulation Policy

Manipulation policy research focuses on developing algorithms that enable robots to perform complex tasks involving object interaction, encompassing diverse scenarios and object types. Current efforts concentrate on improving generalization across unseen objects and environments, often leveraging imitation learning from demonstrations and employing model architectures like transformers and diffusion models to learn effective policies from limited data. These advancements are crucial for creating more robust and adaptable robots capable of operating in unstructured real-world settings, impacting fields ranging from manufacturing and logistics to household assistance.