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The visuomotor system of any animal is critical for its survival, and thedevelopment of a complex one within humans is large factor in our success as aspecies on Earth. This system is an essential part of our ability to adapt toour environment. We use this system continuously throughout the day, whenpicking something up, or walking around while avoiding bumping into objects.Equipping robots with such capabilities will help produce more intelligentlocomotion with the ability to more easily understand their surroundings and tomove safely. In particular, such capabilities are desirable for traversing thelunar surface, as it is full of hazardous obstacles, such as rocks. Theseobstacles need to be identified and avoided in real time. This paper seeks todemonstrate the development of a visuomotor system within a robot fornavigation and obstacle avoidance, with complex rock shaped objectsrepresenting hazards. Our approach uses deep reinforcement learning with onlyimage data. In this paper, we compare the results from several neural networkarchitectures and a preprocessing methodology which includes producing asegmented image and downsampling.

SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images