マルチモーダル経路計画のための自己教師あり輸送コスト推定
Self-supervised cost of transport estimation for multimodal path planning
視覚入力のみから周囲の輸送コストを自己教師あり学習で推定し、走行・飛行・セグウェイ・クロールが可能なロボットM4のエネルギー最適経路計画を実現した研究。
著者: Vincent Gherold, Ioannis Mandralis, Eric Sihite, Adarsh Salagame, Alireza Ramezani, Morteza Gharib
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
