用可微模拟器让动作生成自动符合物理规律，仅凭姿态数据就能训练出能直接部署的机器人动作。
DynaFlow: Dynamics-embedded Flow Matching for Physically Consistent Motion Generation from State-only Demonstrations
- 把可微分物理模拟器嵌入流匹配模型，从动作空间生成轨迹并映射为物理可行的状态轨迹。
- 在真实Go1四足机器人上成功复现多种步态，实现长时间开环控制和不可行演示的物理可执行转化。
- 适合希望从简单姿态数据生成可部署机器人动作的研究者和工程师。
本文提出DynaFlow，一种将可微分模拟器直接嵌入流匹配模型的新框架。通过在动作空间生成轨迹，并利用模拟器将其映射为物理可行的状态轨迹，DynaFlow确保所有输出天然具有物理一致性。该端到端可微架构支持仅基于状态演示进行训练，使模型在生成物理一致的状态轨迹的同时，推断出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，并在真实世界中将生成的动作部署于物理Go1四足机器人上。实验表明，机器人成功复现了数据集中多种步态，实现了长时程开环控制，并能将不可行的运动学演示转化为动态可执行、风格化的行为。硬件实验验证了DynaFlow能从仅有的状态演示中生成可部署、高效的动作，在运动学数据与实际执行之间建立了有效桥梁。
原文摘要 · Abstract (English)
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.
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