@TheSeekerOf42@ErenChenAI The thought of this research is that in future large-scale deployments, unexpected situations are inevitable. We want the robot to still be able to adapt when those situations occur.
We tied our robot up. One policy walks, jumps, and crawls. Restrain it and it finds its own way back to stable walking from a state we never scripted.
Behind it is our founder @RogerXJiang , an ex OpenAI scientist who worked on RLHF, the method behind ChatGPT. We're applying that same idea to robots: train one general policy instead of scripting each move. Follow us for more.
@chris_j_paxton Yes!! In large scale real world deployment, unexpected situations are inevitable. We want our policy to handle them, not just fuction in a controlled lab environment.
@techniahqrobot@LightOrigins@liangpan_t Thanks for the spotlight!!πWe are showcasing that a single policy, the same one that walks and jumps finds its own way back to stable walking from a state we never scripted.