Towards Reinforcement Learning for Space Robotics: Fast Training and Reliable Real-World Transfer
This work demonstrates the use of a fast simulation-based training framework to learn control policies using reinforcement learning.
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Key points
- The hardware requirements are minimal and the simulation-learned policies transfer reliably to the real world.
- With this setup, we show that a quadruped robot can learn to traverse diverse real-world terrains reliably---including in a lunar-analog facility---after less than 20 minutes of training on a single consumer-grade GPU.
- These results provide a glimpse of a future where reinforcement learning is a crucial tool in developing more capable and robust space robotic systems.
Sources (1)
- [1]Towards Reinforcement Learning for Space Robotics: Fast Training and Reliable Real-World TransferarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:10 PM
This work demonstrates the use of a fast simulation-based training framework to learn control policies using reinforcement learning.
The hardware requirements are minimal and the simulation-learned policies transfer reliably to the real world.
Extractive summary: sentences quoted from the sources.
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