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Using reinforcement learning, we trained policies for Boston Dynamics Spot that allow the robot to achieve record running speeds of 11.5 mph (5.2 m/s) — over three times faster than Spot's default max speed.

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RAI Institute

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98,538 görüntüleme • 1 yıl önce •via X (Twitter)

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Loic Argelies profil fotoğrafı
Loic Argelies1 yıl önce

@BostonDynamics Need a gallop mode…

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The Rundown AI1 yıl önce

If you're not learning AI in 2025, you're falling behind. Join 1,000,000+ early adopters reading and learn AI in just 5 minutes a day (for free).

io.net profil fotoğrafı
io.net1 yıl önce

@BostonDynamics At 11.5 mph, Spot is officially faster than most humans. Sleep tight.

Victor Brink profil fotoğrafı
Victor Brink1 yıl önce

@BostonDynamics Great! Now teach it how a change in gait like a cheetah can multiply its top speed further... 110 km/hr (70 mph) This would be epic. The Black Mirror episode featuring a running bot is "Metalhead" for further inspiration.

Elie Aljalbout profil fotoğrafı
Elie Aljalbout1 yıl önce

@BostonDynamics very impressive!

TeslaElon SpaceXFan profil fotoğrafı
TeslaElon SpaceXFan1 yıl önce

@BostonDynamics 🥳👍

Liberty Mint profil fotoğrafı
Liberty Mint1 yıl önce

@BostonDynamics For sustained duration? Faster speeds achieved in sprint?

AI_TechnoKing profil fotoğrafı
AI_TechnoKing1 yıl önce

@BostonDynamics Galloping gears!

Rodomonte profil fotoğrafı
Rodomonte1 yıl önce

@BostonDynamics @AskPerplexity give me calculations on max possible physical speed for a robot of that weight

Pat Dunne profil fotoğrafı
Pat Dunne1 yıl önce

@BostonDynamics Yup RL.. just like motors.. it’s a thing .. welcome BD back to the race

Tom Ramirez profil fotoğrafı
Tom Ramirez1 yıl önce

@BostonDynamics Robot PT

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Watch Spot crouch, jump, climb boxes and leap across gaps, controlled by a neural network trained with reinforcement learning (RL) and multi-expert distillation. Multiple expert policies were trained and distilled together into a single policy that was fine tuned to improve performance over diverse terrains. This work was inspired by ANYmal’s parkour capabilities. The neural network processes depth data from Spot's sensors to construct an understanding of the environment.
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