拥抱流动非定常性：高通量学习平台实现利用涡旋的仿生推进

中文摘要 AI 辅助

生物游泳者和飞行者利用非定常涡旋进行推进，而工程化运载器通常将其作为扰动加以抑制。在机器中学习这种流动利用是困难的，因为真实流体相互作用数据稀缺，且在高维、历史依赖的流动中，非结构化探索不稳定。在此，我们提出REEF，一个协同设计的物理学习框架，它集成了SHOAL——一个用于真实流固相互作用的八通道高通量阵列，以及V-STAR——一个分阶段算法，通过模仿、离线内化和在线适应将这些相互作用转化为策略。在升力型、阻力型和动量射流型推进器中，REEF将可达到的力包络扩展至参数化搜索的两倍以上。粒子图像测速表明，这些增益源于协调的涡旋形成、增长和力投射，而非固定运动-力映射的细化。力训练策略零样本迁移至自由移动机器人，其身体运动改变周围流动，表明REEF学习了可迁移的尾流耦合原理，用于非定常流体中的具身推进。

英文摘要

Biological swimmers and flyers exploit unsteady vortices for propulsion, whereas engineered vehicles usually suppress them as disturbances. Learning such flow exploitation in machines is difficult because real-fluid interaction data are scarce and unstructured exploration is unstable in high-dimensional, history-dependent flows. Here we present REEF, a co-designed physical-learning framework that integrates SHOAL, an eight-channel high-throughput array for real fluid--structure interaction, with V-STAR, a staged algorithm that converts these interactions into policies through imitation, offline internalization, and online adaptation. Across lift-based, drag-based, and momentum-jet propulsors, REEF expands the attainable force envelope to more than twice that of parameterized search. Particle image velocimetry shows that these gains arise from coordinated vortex formation, growth, and force projection, rather than refinement of a fixed motion-to-force mapping. Force-trained policies transfer zero-shot to free-moving robots whose body motion changes the surrounding flow, suggesting that REEF learns transferable wake-coupling principles for embodied propulsion in unsteady fluids.

发表机构

• Zhejiang University（浙江大学）
• Westlake University（西湖大学）
• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
• Queen’s University（女王大学）
• Huanghuai University（黄淮学院）
• University College London（伦敦大学学院）
• Ocean University of China（中国海洋大学）

机构由 AI 辅助整理，请以论文原文为准。