Manipulation Pipeline

A robotic manipulation pipeline automates complex tasks by integrating perception, planning, and control to enable robots to interact with objects. Current research emphasizes developing robust and adaptable pipelines, focusing on techniques like learning from demonstration, code generation from natural language descriptions, and the use of neural implicit models for representing objects and constraints directly from visual input. These advancements leverage various model architectures, including graph neural networks for deformable object manipulation and large language models for task planning, aiming to improve the efficiency and generalizability of robotic manipulation across diverse applications. The resulting improvements in automation have significant implications for industrial settings, household robotics, and warehouse automation.