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

arXiv: https://arxiv.org/abs/2103.14422 · HTML: https://mlsanity.com/paper/2103.14422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Neural SLAM: Learning to Explore with External Memory](https://mlsanity.com/paper/1706.09520/) (2017)
- [The Analysis of Projective Transformation Algorithms for Image Recognition on Mobile Devices](https://mlsanity.com/paper/1912.01401/) (2019)
- [Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning](https://mlsanity.com/paper/1809.10710/) (2018)
- [RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots](https://mlsanity.com/paper/2009.09595/) (2020)
- [PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning](https://mlsanity.com/paper/2003.02655/) (2020)

## Abstract (arXiv)

The visuomotor system of any animal is critical for its survival, and the development of a complex one within humans is large factor in our success as a species on Earth. This system is an essential part of our ability to adapt to our environment. We use this system continuously throughout the day, when picking something up, or walking around while avoiding bumping into objects. Equipping robots with such capabilities will help produce more intelligent locomotion with the ability to more easily understand their surroundings and to move safely. In particular, such capabilities are desirable for traversing the lunar surface, as it is full of hazardous obstacles, such as rocks. These obstacles need to be identified and avoided in real time. This paper seeks to demonstrate the development of a visuomotor system within a robot for navigation and obstacle avoidance, with complex rock shaped objects representing hazards. Our approach uses deep reinforcement learning with only image data. In this paper, we compare the results from several neural network architectures and a preprocessing methodology which includes producing a segmented image and downsampling.