Tabletop Manipulation

Tabletop manipulation research focuses on enabling robots to perform complex tasks involving objects on a table, driven by instructions (e.g., natural language commands or visual demonstrations) and guided by visual perception. Current efforts concentrate on improving the robustness and generalizability of manipulation systems, often employing multimodal models that integrate visual and linguistic information, and leveraging techniques like keypoint-based grounding and task and motion planning frameworks to achieve long-horizon, fine-grained control. These advancements are significant for advancing robotics capabilities in areas such as household assistance, industrial automation, and space exploration, where precise and adaptable manipulation is crucial.