Generalizable Manipulation

Generalizable manipulation aims to enable robots to perform a wide variety of manipulation tasks on unseen objects and in novel environments, overcoming the limitations of task-specific approaches. Current research focuses on leveraging large language models and vision-language models to interpret instructions and plan actions, often combined with advanced techniques like diffusion models for depth estimation and object representation, and imitation learning from both robotic and human demonstrations. 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 assistive robotics and home automation.