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Ville🤖

@VilleKuosmanen • 3,196 subscribers

gentleman scientist 🤖 @voyagerobotics

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I rented an H100 and fine-tuned Physical Intelligence ‘s pi0 overnight. We’re still far from fully utilising the potential of this model, but it produced my first successful rollout!

I rented an H100 and fine-tuned Physical Intelligence ‘s pi0 overnight. We’re still far from fully utilising the potential of this model, but it produced my first successful rollout!

66,365 次观看

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MolmoPoint from Ai2 was a recent foundation model release that feels criminally underrated to me, while not a robotics model it has great potential for various use cases labels are great even for wrist view!

MolmoPoint from Ai2 was a recent foundation model release that feels criminally underrated to me, while not a robotics model it has great potential for various use cases labels are great even for wrist view!

19,956 次观看 • 2 个月前

Do AI robots see the world like we do? I dove head first into latent space to uncover the attention maps that show how my robot sees and understands the world.

Do AI robots see the world like we do? I dove head first into latent space to uncover the attention maps that show how my robot sees and understands the world.

44,087 次观看 • 1 年前

a normal day in Paris 🇫🇷 Haixuan Xavier Tao

a normal day in Paris 🇫🇷 Haixuan Xavier Tao

36,608 次观看 • 1 年前

After fixing a few bugs with inference code, I finally have a working pi0 set up! Fine-tuned overnight on single task data, it learns rough controls and no longer outputs unsafe actions. But mastering the task and form factor will require much more compute. Stay tuned!

After fixing a few bugs with inference code, I finally have a working pi0 set up! Fine-tuned overnight on single task data, it learns rough controls and no longer outputs unsafe actions. But mastering the task and form factor will require much more compute. Stay tuned!

33,242 次观看 • 1 年前

I've been working on neural safety systems for robotics. v1 identifies situations as risky/not risky correctly but can't tell between success & failure. Quick wins will come from more data & better labels but the method needs to improve as well

I've been working on neural safety systems for robotics. v1 identifies situations as risky/not risky correctly but can't tell between success & failure. Quick wins will come from more data & better labels but the method needs to improve as well

18,453 次观看 • 11 个月前

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