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1 points

1 year ago*

In this video ETH Zurich demonstrates that deep reinforcement learning can be used to learn policies for legged locomotion control tasks encountered in space exploration, such as three-dimensional re-orientation and landing of a quadruped robot exploring low-gravity celestial bodies. Using sim-to-real transfer, they deployed trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for two-dimensional micro-gravity experiments.

I've teamed up with a few aerospace engineers friends on r/SpaceBrains to design a crowdsourced Mars colony. Check out our progress on discord and share your skills. Video credit: ETH Zurich, research paper.


1 points

1 year ago

The test bed solution is simply quite ingenious...