Manipulation Primitive

Manipulation primitives are fundamental actions in robotics, representing atomic units of object manipulation used to build complex robotic behaviors. Current research focuses on learning and optimizing sequences of these primitives, often using hierarchical reinforcement learning and algorithms like Monte Carlo tree search, to achieve complex tasks such as object rearrangement and assembly. This work leverages parameterized primitives and differentiable scene representations to improve efficiency and robustness, particularly in scenarios with occlusions or varied object properties. Advances in this area are crucial for enabling robots to perform a wider range of dexterous manipulation tasks in unstructured environments.