Self-supervised cost of transport estimation for multimodal path planning
This work addresses navigation efficiency for multi-modal robotic platforms, though it appears incremental as it builds on existing self-supervised and path planning methods.
The paper tackled the problem of enabling autonomous robots to choose energetically optimal paths by developing a self-supervised learning method that estimates the cost of transport from vision inputs, applied to a multi-modal robot and shown to accurately assign costs to different environments like grass vs. smooth road with low computational cost on an Nvidia Jetson Orin Nano.
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.