Learning long-horizon robotic manipulation requires jointly achieving expressive behavior modeling, real-time inference, and stable execution, which remains challenging for existing generative policies. Diffusion-based approaches provide strong modeling capacity but typically incur high inference latency, while flow matching enables fast one-step generation yet often leads to unstable execution when applied directly in the raw action space. We propose LG-Flow Policy, a trajectory-level imitation learning framework that performs flow matching in a continuous latent action space. By encoding action sequences into temporally regularized latent trajectories and learning an explicit latent-space flow, the proposed approach decouples global motion structure from low-level control noise, resulting in smooth and reliable long-horizon execution. LG-Flow Policy further incorporates geometry-aware point cloud conditioning and execution-time multimodal modulation, with visual cues evaluated as a representative modality in real-world settings. Experimental results in simulation and on physical robot platforms demonstrate that LG-Flow Policy achieves near single-step inference, substantially improves trajectory smoothness and task success over flow-based baselines operating in the raw action space, and remains significantly more efficient than diffusion-based policies.

翻译：学习长时程机器人操作需要同时实现高表现力的行为建模、实时推理和稳定执行，这对现有的生成式策略而言仍具挑战性。基于扩散的方法提供了强大的建模能力，但通常带来较高的推理延迟；而流匹配虽能实现快速单步生成，但直接在原始动作空间应用时往往导致执行不稳定。我们提出了LG-Flow策略，这是一种在连续潜在动作空间执行流匹配的轨迹级模仿学习框架。通过将动作序列编码为经过时序正则化的潜在轨迹，并学习显式的潜在空间流，该方法将全局运动结构与底层控制噪声解耦，从而实现了平滑可靠的长时程执行。LG-Flow策略进一步结合了几何感知的点云条件输入以及执行时的多模态调制，其中视觉线索在真实世界设置中被评估为代表模态。仿真和物理机器人平台上的实验结果表明，LG-Flow策略实现了接近单步的推理速度，相较于在原始动作空间操作的基于流的基线方法，显著提升了轨迹平滑度和任务成功率，并且其效率仍显著高于基于扩散的策略。