Manipulation Strategy

Manipulation strategy research focuses on enabling robots to effectively interact with and modify their environment, encompassing tasks from grasping objects to complex assembly. Current efforts concentrate on developing robust and adaptable strategies using model-predictive control, behavior trees, and deep learning architectures like transformers and large vision-language models, often incorporating techniques like imitation learning and reinforcement learning to improve efficiency and generalization. This field is crucial for advancing robotics, particularly in areas like industrial automation, assistive technologies, and space exploration, where reliable and versatile manipulation is essential.