Kallol Saha

🌍 Excited to share T2-z: Test-Time Optimization of Environment Latents for Robotic World Models! Same observation, same action, different environments → different outcomes. How can world models adapt to hidden environmental factors like friction, mass, or physical rules? 💡 T2-z conditions world models on optimizable environment latents. T2-z directly optimizes the conditioning latents through world prediction, without supervision of what these factors are, what values they take, or how many factors exist. 📊 Meaningful latent structure emerges purely through world prediction! For compounded environmental factors (mass × friction), PCA of the learned latents reveals a two-dimensional structure that closely reflects the underlying factor distribution, despite the model never knowing how many factors are present or what they represent. 🤖 Across 7 simulated settings and 4 real-world robotic tasks, T2-z: •Reduces object prediction error from 15.20 to 7.00 (54% reduction). •Improves action success from 33.73% to 72.96% (+39.23 percentage points). 🌐 Project: t2-z-latent-optimization.git… 📄 Paper: arxiv.org/abs/2610.22364 💻 Code: github.com/T2-z-latent-optim… Collab with Chenyuan, @RavenHuang4 @YunfanJiang @wenlong_huang @davheld @vitorguizilini @yuewang314 @drfeifei @jiajunwu_cs , leo and @YejinChoinka