AMD Robot Training Changes Beat Baseline Across Five Training Runs

A revised robot training setup beat a supervised baseline in all five simulator runs, although its video world model still portrayed some failed actions as successes. The Oct. 20 study tested the π0.5 policy on RoboTwin’s adjust_bottle task. Boundless World Model (BWM) generated videos from the policy’s actions, and a reward classifier scored the depicted outcomes before the trained policy was tested in the simulator. A model that shows a missed grasp as successful can reward an action that did not work. BWM portrayed 82.5% of the simulator failures as successes.

Source: TokenPost