Sequential Manipulation

Sequential manipulation research focuses on enabling robots and AI systems to perform complex tasks involving a series of coordinated actions, often requiring interaction with multiple objects and environments. Current efforts concentrate on improving planning algorithms, particularly those integrating multimodal data (visual, tactile, force) with large language models or leveraging model predictive control and reinforcement learning techniques to handle uncertainty and sparse rewards. This field is crucial for advancing robotics capabilities in areas like manufacturing, healthcare, and daily assistance, driving progress in both efficient task planning and robust execution in dynamic, real-world settings.