Manipulation Planning

Manipulation planning in robotics focuses on generating sequences of robot actions to achieve complex tasks involving object interaction, encompassing grasping, pushing, and other contact-based maneuvers. Current research emphasizes efficient planning algorithms, including those leveraging neural networks (e.g., graph neural networks, diffusion models) and optimal transport methods, to handle high-dimensional state spaces and diverse manipulation modalities (prehensile, non-prehensile, in-hand). These advancements are crucial for enabling robots to operate effectively in unstructured environments and perform tasks requiring dexterity and adaptability, with applications ranging from manufacturing and agriculture to assistive robotics. The integration of visual and tactile feedback, along with the use of learned representations of objects and their affordances, is a key trend driving progress in this field.