用视觉自监督学习估算路径能耗，让机器人自动选最优行进方式。
Self-supervised cost of transport estimation for multimodal path planning
- 仅用视觉输入，自监督学习预测环境能耗成本。
- 实测可区分草地、平路等不同地形的能耗差异。
- 低计算开销，可在Jetson Orin Nano上实时运行。
自主机器人在真实环境中常需决定如何最优导航。本文解决一个具体问题：给定高层目标和环境信息，机器人如何自主选择能量最省的路径？为此，我们提出一种自监督学习方法，仅通过视觉输入即可估计周围环境的运输成本。该方法应用于多模态移动机器人M4（具备行驶、飞行、平衡车、爬行能力）。在真实世界部署中，系统能准确区分草地与光滑路面等不同环境的运输成本。此外，方法计算开销极低，可部署于Nvidia Jetson Orin Nano机器人计算单元上。本工作有望使多模态机器人平台充分发挥导航与探索潜力。
原文摘要 · Abstract (English)
Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about the surroundings? To tackle this problem we developed a self-supervised learning method that allows the robot to estimate the cost of transport of its surroundings using only vision inputs. We apply our method to the multi-modal mobility morphobot (M4), a robot that can drive, fly, segway, and crawl through its environment. By deploying our system in the real world, we show that our method accurately assigns different cost of transports to various types of environments e.g. grass vs smooth road. We also highlight the low computational cost of our method, which is deployed on an Nvidia Jetson Orin Nano robotic compute unit. We believe that this work will allow multi-modal robotic platforms to unlock their full potential for navigation and exploration tasks.
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