Generalizable Multistage Assembly via One-Shot Category-Level Demonstration

Imitation learning offers a flexible approach for robot skill acquisition, enabling robots to learn complex tasks directly from demonstrations. However, most existing methods require a large number of demonstrations, whereas humans typically only need one or a few demonstrations. This discrepancy results in significant time consumption for data collection. Furthermore, these methods often assume that test scenarios will always be identical to the demonstration, which can lead to substantial performance degradation when facing novel scenarios, such as manipulating objects from the same category but with different shapes and sizes, or encountering object collisions during manipulation. To address these challenges, we propose a generalized multistage manipulation network for category-level robot assembly tasks. This network allows a robot to learn a multistage screw-nut assembly task from a single demonstration and generalize to new object instances with varying shapes and sizes. Specifically, the network uses category-level pose estimation to extract manipulation trajectories from the demonstration and applies manipulation-pose generalization to transfer these trajectories to novel instances. In addition, real-time action correction adjusts the trajectory based on real-time force feedback, enabling the robot to adapt to unexpected collisions during execution. We validate our method through experiments in both simulation and real-world environments, verifying its effectiveness and flexibility.