AURA：多模态共享自主控制用于真实世界城市导航
AURA: Multimodal Shared Autonomy for Real-World Urban Navigation
AI总结：
AURA通过将城市导航分解为高层人类指令和低层AI控制，减少人工操作负担，提升导航稳定性，并在真实世界中实现44%的接管减少。
摘要：
复杂城市环境中的长视距导航严重依赖连续的人工操作，导致疲劳、效率降低和安全问题。共享自主控制，即视觉-语言AI代理与人类操作员协作操控移动机器，为解决这些问题提供了有前景的解决方案。然而，现有共享自主控制方法通常要求人类和AI在相同的操作空间内操作，导致认知负担大。我们提出了Assistive Urban Robot Autonomy（AURA），一种新的多模态框架，将城市导航分解为高层人类指令和低层AI控制。AURA结合了空间感知指令编码器，以对齐各种人类指令与视觉和空间上下文。为了促进训练，我们构建了MM-CoS，一个大规模数据集，包含遥控和视觉-语言描述。在仿真和真实世界中的实验表明，AURA能够有效遵循人类指令，减少人工操作负担，提高导航稳定性，并实现在线适应。此外，在相似接管条件下，我们的共享自主框架将接管频率减少了超过44%。演示视频和更多细节可在项目页面中找到。
英文摘要：
Long-horizon navigation in complex urban environments relies heavily on continuous human operation, which leads to fatigue, reduced efficiency, and safety concerns. Shared autonomy, where a Vision-Language AI agent and a human operator collaborate on maneuvering the mobile machine, presents a promising solution to address these issues. However, existing shared autonomy methods often require humans and AI to operate within the same action space, leading to high cognitive overhead. We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control. AURA incorporates a Spatial-Aware Instruction Encoder to align various human instructions with visual and spatial context. To facilitate training, we construct MM-CoS, a large-scale dataset comprising teleoperation and vision-language descriptions. Experiments in simulation and the real world demonstrate that AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation. Moreover, under similar takeover conditions, our shared autonomy framework reduces the frequency of takeovers by more than 44%. Demo video and more detail are provided in the project page.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
