Here we see our little GoodDog, automatically avoiding an obstacle. I’ll be honest, this only works fully about 25% of the time, the other 25% of the time it may move towards a direction which could potentially avoid a collision, and the rest of the time it will just fail to avoid it.

However, still impressive, as this is purely end-to-end trained on around 6 hours of real-world robot data. The reward function gives a reward signal when detectable objects (from a YOLOv5 network) are in view, with a bonus for looking at humans. Additionally, as the human, you are able to press a button on your phone when the robot is training, to send an additional reward or punishment signal. We often do this when the robot has gotten stuck on a chair or corner.

(Video from Feb 2nd, using the usual-paper-495 checkpoint)