Flow Policy: Generalizable Visuomotor Policy Learning via Flow Matching

An effective method to improve the performance of robot imitation learning is generating sequence models such as the diffusion model, which is simulated-based and currently lacking flexibility. In this article, we introduce a novel and general methodology called flow policy to address this challenge. Specifically, the flow policy integrates flow matching, a simulated-free sequence modeling method, into robot behavior cloning. To fully unlock the potential of flow matching for imitation learning on robots, we present a set of key technical contributions, including a noise scheduler based on higher order trigonometric functions, time-varying noise, and a UDiT1d network with self-supervised learning. To validate its efficacy, we implemented the technique across several simulation benchmarks and real-world environments, achieving superior performance with fewer parameters compared to the diffusion policy. Notably, our method resulted in a 2.41% improvement in the 2-D simulation task (36 000 tests), a 6.21% improvement in the 3-D simulation task (36 000 tests), and a remarkable 34.10% improvement in the real-world task (90 tests).