Title: From Tokens to Actions: Discrete Flow Matching for Visuomotor Policy Learning
Primary area: applications to robotics, autonomy, planning
Keywords: Robot Manipulation, Behavior Cloning, Generative Modeling

Abstract: Although actions in the physical world are inherently continuous, representing them in a discrete space can unlock stability, efficiency, and multimodality in policy learning. We present Discrete Flow Matching Policy (DFMP), a novel method that learns continuous robot actions in a discrete space using score-based generative modeling. DFMP formulates action generation as a Continuous-Time Markov Chain, learning transition probabilities over action tokens. Through this, DFMP unifies three desirable properties: (i) stable optimization through flow-matching objectives, (ii) multimodal behavior modeling via probabilistic branching between tokens, and (iii) fast inference. To bridge continuous control with discrete representations, we systematically study tokenization schemes and analyze their trade-offs, proposing the optimal approach for real world robot policies. We thoroughly evaluate DFMP across many challenging simulated manipulation benchmarks and two real-world robot deployments, showing that our approach provides not only strong task performance, but also better scalability and robustness compared to existing continuous-space methods. These results position DFMP as a new, principled approach to efficient, robust, and multimodal visuomotor policy learning, advancing the integration of discrete generative modeling into real-world robotics. Videos and code are provided on the project page https://dfm-policy.github.io.