Mobile Manipulation

Mobile manipulation research focuses on enabling robots to seamlessly integrate locomotion and manipulation capabilities for complex tasks in unstructured environments. Current efforts concentrate on developing robust control strategies, often employing reinforcement learning, imitation learning, and large language models to coordinate whole-body motion, handle articulated objects, and adapt to dynamic scenes. This field is crucial for advancing autonomous robotics, with applications ranging from domestic service robots to industrial automation and search and rescue operations. The development of efficient and generalizable methods for mobile manipulation is a key challenge driving ongoing research.