Hugging Face
Models
Datasets
Spaces
Community
Docs
Enterprise
Pricing
Log In
Sign Up
3
David Khosid
PRO
davidkh
Follow
0 followers
·
3 following
davidkh
davidkh1
AI & ML interests
Intelligent robotics, reinforcement learning, physical simulations, generative art
Recent Activity
updated
a dataset
14 days ago
davidkh/so101_orange_ron_2
published
a dataset
14 days ago
davidkh/so101_orange_ron_2
reacted
to
IliaLarchenko
's
post
with 🔥
3 months 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
View all activity
Organizations
davidkh
's datasets
1
Sort: Recently updated
davidkh/so101_orange_ron_2
Viewer
•
Updated
14 days ago
•
1.8k
•
148