用模仿学习让轮椅更顺滑跟行，提升行动不便者体验
Follow-Me in Micro-Mobility with End-to-End Imitation Learning
- 端到端模仿学习构建跟行控制器
- 实测舒适度达当前最优水平
- 适用于真实场景的生产级部署
自主微型移动平台在大型室内或高度动态的城市环境中面临挑战。尽管社交导航算法已取得显著进展，但优化用户舒适度和整体用户体验（而非传统机器人指标如时间或距离）仍研究不足，而这对于商业应用至关重要。本文展示模仿学习如何生成比以往手动调参控制器更平滑、整体表现更优的控制策略。我们验证了DAAV自主轮椅在跟随模式下的状态领先舒适度，该模式中轮椅跟随一名协助行动不便者的操作员。论文分析了不同神经网络架构在端到端控制中的适用性，并证明其可在实际生产环境中部署。
原文摘要 · Abstract (English)
Autonomous micro-mobility platforms face challenges from the perspective of the typical deployment environment: large indoor spaces or urban areas that are potentially crowded and highly dynamic. While social navigation algorithms have progressed significantly, optimizing user comfort and overall user experience over other typical metrics in robotics (e.g., time or distance traveled) is understudied. Specifically, these metrics are critical in commercial applications. In this paper, we show how imitation learning delivers smoother and overall better controllers, versus previously used manually-tuned controllers. We demonstrate how DAAV's autonomous wheelchair achieves state-of-the-art comfort in follow-me mode, in which it follows a human operator assisting persons with reduced mobility (PRM). This paper analyzes different neural network architectures for end-to-end control and demonstrates their usability in real-world production-level deployments.
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