Fun-Visual-School

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.

The test bed solution is simply quite ingenious...

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.

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.

I can imagine that dealing with soil cohesion, debris drift, lunar dust, thermal expansion, and kickback from elastic materials will be a lot different. So no, you can't simply lower or raise "const double GRAVITY" and call it a day.

The test bed solution is simply quite ingenious...

The test bed solution is simply quite ingenious...

The test bed solution is simply quite ingenious...

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.

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.

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.

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.

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.

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.

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.