Robotics

TL;DR Diffusion Policy borrows the denoising trick from Stable Diffusion (start with pure noise, gradually refine) and applies it to a short horizon of robot actions instead of pixels. It crushes classic behavior cloning baselines on manipulation benchmarks, but the sampling loop is slow and still blind to out-of-distribution situations. Recent follow-ups (OneDP, RNR-DP, Consistency Policy, Diff-DAgger) attack those pain points with distillation, smarter noise scheduling, and uncertainty heads. ...

Why the Next AI Breakthrough Has to Move Atoms, Not Words Imagine coming home after a long day to find your apartment tidied, dinner prepared, and everything in its place, not because you hired help, but because your home’s AI system physically handled these tasks while you were away. This isn’t science fiction anymore; it’s the frontier of artificial intelligence that’s rapidly approaching our everyday lives. The Physical AI Challenge? When computer scientist Alan Turing proposed his famous test for machine intelligence in the 1950s, he focused on conversation. Could a computer fool a human through text chat?1 Fast forward to today, and AI chatbots have become so convincing that this once-impossible challenge feels almost ordinary. ...