用快速推理的流模型提升机器人长程操作的效率与精度
ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows
- 将可修正流引入SE(3)等变扩散模型，实现单步推理
- 单次推断即达基线100步效果，绘画任务误差降48.5%
- 适合追求高效、高精度生成策略的机器人应用
在非结构化环境中进行机器人操作需要生成鲁棒且长时程的轨迹级策略，依赖感知观测并受益于数据高效的SE(3)-等变扩散模型。然而，这类模型存在推理时间成本高的问题。受可修正流推理效率的启发，本文将修正机制引入SE(3)扩散模型，提出ReSeFlow（Rectifying SE(3)-Equivariant Policy Learning Flows），实现快速、测地线一致且计算最少的策略生成。关键组件均采用SE(3)-等变网络，保持旋转与平移对称性，确保刚体运动下的稳健泛化。在模拟基准测试中，仅需一次推理的ReSeFlow表现优于基线方法，绘画任务误差降低48.5%，旋转三角任务降低21.9%，相较基线100步推理效果更优。该方法融合了SE(3)等变性与可修正流优势，推动生成式策略模型在真实场景中的数据与推理效率应用。
原文摘要 · Abstract (English)
Robotic manipulation in unstructured environments requires the generation of robust and long-horizon trajectory-level policy with conditions of perceptual observations and benefits from the advantages of SE(3)-equivariant diffusion models that are data-efficient. However, these models suffer from the inference time costs. Inspired by the inference efficiency of rectified flows, we introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, i.e., Rectifying SE(3)-Equivariant Policy Learning Flows, providing fast, geodesic-consistent, least-computational policy generation. Crucially, both components employ SE(3)-equivariant networks to preserve rotational and translational symmetry, enabling robust generalization under rigid-body motions. With the verification on the simulated benchmarks, we find that the proposed ReSeFlow with only one inference step can achieve better performance with lower geodesic distance than the baseline methods, achieving up to a 48.5% error reduction on the painting task and a 21.9% reduction on the rotating triangle task compared to the baseline's 100-step inference. This method takes advantages of both SE(3) equivariance and rectified flow and puts it forward for the real-world application of generative policy learning models with the data and inference efficiency.
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