提出ActionFlow，让机器人更懂空间关系，少靠示范就能完成复杂操作。
ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching
- 用SE(3)不变的Transformer建模观察与动作间的相对空间关系。
- 结合流匹配生成高质量动作序列，推理速度快，适合实时控制。
- 在模拟和真实机器人任务中表现优异，支持空间对称性与高效执行。
空间理解是多数机器人任务的关键，尤其在需要泛化时。尽管深度生成模型在复杂操作任务中表现卓越，但缺乏编码观测与动作间复杂空间关系的表示，常导致空间泛化能力受限，需大量示范数据。为此，本文提出新型策略框架ActionFlow，通过引入空间对称性先验，在生成丰富动作序列的同时增强空间推理能力。在表示层面，ActionFlow采用SE(3)不变变压器架构，基于观测与动作间的相对SE(3)位姿实现智能空间推理。在动作生成方面，采用前沿的流匹配技术，该方法以高保真样本生成和快速推理著称，适用于反馈控制。二者结合使策略具备强空间与局部性偏置及SE(3)-等变的动作生成能力。实验表明，ActionFlow及其两大核心组件在多个仿真与真实世界机器人操控任务中均表现有效，证实了其可获得等变、精准且高效的策略。项目主页：https://flowbasedpolicies.github.io/
原文摘要 · Abstract (English)
Spatial understanding is a critical aspect of most robotic tasks, particularly when generalization is important. Despite the impressive results of deep generative models in complex manipulation tasks, the absence of a representation that encodes intricate spatial relationships between observations and actions often limits spatial generalization, necessitating large amounts of demonstrations. To tackle this problem, we introduce a novel policy class, ActionFlow. ActionFlow integrates spatial symmetry inductive biases while generating expressive action sequences. On the representation level, ActionFlow introduces an SE(3) Invariant Transformer architecture, which enables informed spatial reasoning based on the relative SE(3) poses between observations and actions. For action generation, ActionFlow leverages Flow Matching, a state-of-the-art deep generative model known for generating high-quality samples with fast inference - an essential property for feedback control. In combination, ActionFlow policies exhibit strong spatial and locality biases and SE(3)-equivariant action generation. Our experiments demonstrate the effectiveness of ActionFlow and its two main components on several simulated and real-world robotic manipulation tasks and confirm that we can obtain equivariant, accurate, and efficient policies with spatially symmetric flow matching. Project website: https://flowbasedpolicies.github.io/
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