Hugging Face's logo
PommesPeter's picture
9 16 37

PommesPeter

PommesPeter
HAUZachery's profile picture ltim's profile picture 21world's profile picture
·

AI & ML interests

MM-LLM

Recent Activity

reacted to IliaLarchenko's post with 👍 1 day ago
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
reacted to IliaLarchenko's post with 🔥 1 day ago
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
updated a model 1 day ago
PommesPeter/dp_ckpts
View all activity

Organizations

ZeroGPU Explorers's profile picture VL-Group's profile picture MagicBot's profile picture