This paper introduces DynaFlow, a novel framework that embeds a differentiable simulator directly into a flow matching model. By generating trajectories in the action space and mapping them to dynamically feasible state trajectories via the simulator, DynaFlow ensures all outputs are physically consistent by construction. This end-to-end differentiable architecture enables training on state-only demonstrations, allowing the model to simultaneously generate physically consistent state trajectories while inferring the underlying action sequences required to produce them. We demonstrate the effectiveness of our approach through quantitative evaluations and showcase its real-world applicability by deploying the generated actions onto a physical Go1 quadruped robot. The robot successfully reproduces diverse gait present in the dataset, executes long-horizon motions in open-loop control and translates infeasible kinematic demonstrations into dynamically executable, stylistic behaviors. These hardware experiments validate that DynaFlow produces deployable, highly effective motions on real-world hardware from state-only demonstrations, effectively bridging the gap between kinematic data and real-world execution.

翻译：本文提出DynaFlow，一种将可微分仿真器直接嵌入流匹配模型的新型框架。通过在动作空间生成轨迹并通过仿真器将其映射为动力学可行的状态轨迹，DynaFlow从构造上确保所有输出均满足物理一致性。这种端到端可微分架构支持仅基于状态演示进行训练，使模型能够同时生成物理一致的状态轨迹，并推断产生这些轨迹所需的底层动作序列。我们通过定量评估验证了方法的有效性，并通过将生成的动作部署到实体Go1四足机器人上展示了其实用性。该机器人成功复现了数据集中多种步态，在开环控制中执行长时程运动，并将运动学不可行的演示转化为动力学可执行的风格化行为。这些硬件实验证实，DynaFlow能够仅基于状态演示在真实硬件上生成可直接部署的高效运动，有效弥合了运动学数据与实际执行之间的鸿沟。