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 offer strong modeling capacity but incur high inference latency, while flow matching enables fast, near-single-step generation yet often suffers from unstable execution when operating directly in the raw action space. We propose Continuous Latent Action Flow Policy (CoLA-Flow Policy), a trajectory-level imitation learning framework that performs flow matching in a continuous latent action space. By encoding action sequences into temporally coherent latent trajectories and learning an explicit latent-space flow, CoLA-Flow Policy decouples global motion structure from low-level control noise, enabling smooth and reliable long-horizon execution. The framework further integrates geometry-aware point cloud conditioning and execution-time multimodal modulation, using visual cues as a representative modality to enhance real-world robustness. Experiments in simulation and on real robots show that CoLA-Flow Policy achieves near-single-step inference, improves trajectory smoothness by up to 93.7% and task success by up to 25 percentage points over raw action-space flow baselines, while remaining significantly faster than diffusion-based policies.

翻译：学习长时间跨度的机器人操作需要同时实现富有表现力的行为建模、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。基于扩散的方法虽具备强大的建模能力，但推理延迟高；而流匹配尽管能实现近乎单步的快速生成，但直接在原始动作空间操作时往往面临执行不稳定的问题。我们提出连续潜动作流策略（CoLA-Flow Policy），这是一种在连续潜动作空间中进行流匹配的轨迹级模仿学习框架。通过将动作序列编码为时间一致的潜轨迹，并学习显式的潜空间流，CoLA-Flow策略将全局运动结构与底层控制噪声解耦，从而实现了平滑且可靠的长时间跨度执行。该框架进一步集成了几何感知点云条件约束和执行时多模态调制（以视觉线索作为代表性模态），以增强在真实世界中的鲁棒性。在仿真和真实机器人上的实验表明，CoLA-Flow策略实现了近乎单步的推理，与原始动作空间的流基线相比，轨迹平滑度提升了高达93.7%，任务成功率提升了高达25个百分点，同时其速度显著快于基于扩散的策略。