Self-supervised cost of transport estimation for multimodal path planning

TL;DR

This paper introduces a self-supervised learning approach enabling multimodal robots to estimate transport costs from vision, optimizing navigation by selecting energetically efficient paths in real-world environments.

Contribution

A novel self-supervised method for estimating transport costs using vision inputs, applied to a multi-modal robot, enabling autonomous, energy-efficient path planning.

Findings

Accurately estimates transport costs for different terrains

Operates efficiently on low-power hardware

Improves autonomous navigation in diverse environments

Abstract

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.