Kavli Affiliate: Morteza Gharib
| First 5 Authors: Vincent Gherold, Ioannis Mandralis, Eric Sihite, Adarsh Salagame, Alireza Ramezani
| Summary:
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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