content.raw: I am presenting Decoder-Only Transformer (DOT) Policy a simple Behavioral Control policy that outperforms SOTA models on two simple benchmark tasks:
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content.value: I am presenting Decoder-Only Transformer (DOT) Policy a simple Behavioral Control policy that outperforms SOTA models on two simple benchmark tasks:
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content.raw: ✅ PushT (pushing an object to a goal) – 84% success on keypoints, 74% on images (previous best: 75% / 69%)
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content.value: ✅ PushT (pushing an object to a goal) – 84% success on keypoints, 74% on images (previous best: 75% / 69%)
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content.raw: ✅ ALOHA Insert (precise bimanual insertion) – 30% success (previous best: ~21%)
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content.value: ✅ ALOHA Insert (precise bimanual insertion) – 30% success (previous best: ~21%)
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content.raw: The best part? DOT is much smaller (sometimes 100 times less parameters) than previous SOTA models, trains faster, and avoids complexity:
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content.value: The best part? DOT is much smaller (sometimes 100 times less parameters) than previous SOTA models, trains faster, and avoids complexity:
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content.raw: 🚫 No generative models (Diffusion, VAE, GANs)
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content.value: 🚫 No generative models (Diffusion, VAE, GANs)
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content.raw: 🚫 No discretization/tokenization of actions
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content.value: 🚫 No discretization/tokenization of actions
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content.raw: 🚫 No reinforcement learning or multi-stage training
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content.value: 🚫 No reinforcement learning or multi-stage training
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content.raw: ✅ Just learns from human demos, plain and simple
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content.value: ✅ Just learns from human demos, plain and simple
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content.raw: This is still early — more complex real-life tasks need testing, and no guarantees it will actually work well there, but I think it's interesting to share. Sometimes, simpler approaches can be just as effective (or even better) than complex ones.
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content.value: This is still early — more complex real-life tasks need testing, and no guarantees it will actually work well there, but I think it's interesting to share. Sometimes, simpler approaches can be just as effective (or even better) than complex ones.
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content.raw: 🔗 Open-source code and detailed description: 
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content.value: 🔗 Open-source code and detailed description: 
content.href: https://github.com/IliaLarchenko/dot_policy
content.raw: https://github.com/IliaLarchenko/dot_policy
content.type: link
content.type: new_line
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content.raw: Trained models on Hugging Face:
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content.value: Trained models on Hugging Face:
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content.raw: https://huggingface.co/IliaLarchenko/dot_pusht_keypoints
content.id: IliaLarchenko/dot_pusht_keypoints
content.type: model
content.type: resource
content.url: https://huggingface.co/IliaLarchenko/dot_pusht_keypoints
content.type: new_line
content.raw: https://huggingface.co/IliaLarchenko/dot_pusht_images
content.id: IliaLarchenko/dot_pusht_images
content.type: model
content.type: resource
content.url: https://huggingface.co/IliaLarchenko/dot_pusht_images
content.type: new_line
content.raw: https://huggingface.co/IliaLarchenko/dot_bimanual_insert
content.id: IliaLarchenko/dot_bimanual_insert
content.type: model
content.type: resource
content.url: https://huggingface.co/IliaLarchenko/dot_bimanual_insert
rawContent: I am presenting Decoder-Only Transformer (DOT) Policy a simple Behavioral Control policy that outperforms SOTA models on two simple benchmark tasks:
  
  ✅ PushT (pushing an object to a goal) – 84% success on keypoints, 74% on images (previous best: 75% / 69%)
  ✅ ALOHA Insert (precise bimanual insertion) – 30% success (previous best: ~21%)
  
  The best part? DOT is much smaller (sometimes 100 times less parameters) than previous SOTA models, trains faster, and avoids complexity:
  🚫 No generative models (Diffusion, VAE, GANs)
  🚫 No discretization/tokenization of actions
  🚫 No reinforcement learning or multi-stage training
  ✅ Just learns from human demos, plain and simple
  
  This is still early — more complex real-life tasks need testing, and no guarantees it will actually work well there, but I think it's interesting to share. Sometimes, simpler approaches can be just as effective (or even better) than complex ones.
  
  🔗 Open-source code and detailed description: https://github.com/IliaLarchenko/dot_policy
  
  Trained models on Hugging Face:
  https://huggingface.co/IliaLarchenko/dot_pusht_keypoints
  https://huggingface.co/IliaLarchenko/dot_pusht_images
  https://huggingface.co/IliaLarchenko/dot_bimanual_insert
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