Goal Conditioned Manipulation

Goal-conditioned manipulation focuses on training robots to perform complex tasks involving object manipulation, guided by a specific target state or goal. Current research emphasizes learning robust and generalizable manipulation policies, often using reinforcement learning approaches with structured representations (like entity-centric models) or iterative methods that refine actions based on observed dynamics. These advancements are improving robotic dexterity and adaptability, with applications ranging from industrial automation to assistive robotics, particularly in handling deformable objects and diverse environments. The ability to generalize to unseen objects and scenarios, even across different robot embodiments, is a key area of ongoing development.