Galbot Team

This work systematically evaluates GPT-6 Astra as an embodied policy across six domains—gripper manipulation, dexterous manipulation, mobile manipulation, navigation, locomotion, and humanoid loco-manipulation—comparing direct control with hybrid control that cooperates with learned policies or whole-body controllers, finding leading navigation results (92% on RxR instruction following, 82% on HM3D object search), hybrid success of 48% on RoboDojo, 50% on DexJoCo, and 38.7% on RoboCasa365, while direct in-hand control and dense-reference locomotion remain unreliable, with substantial token use and inference latency recorded.

This work systematically evaluates GPT-6 Astra as an embodied policy across six domains—gripper manipulation, dexterous manipulation, mobile manipulation, navigation, locomotion, and humanoid loco-manipulation—comparing direct control with hybrid control that cooperates with learned policies or whole-body controllers, finding leading navigation results (92% on RxR instruction following, 82% on HM3D object search), hybrid success of 48% on RoboDojo, 50% on DexJoCo, and 38.7% on RoboCasa365, while direct in-hand control and dense-reference locomotion remain unreliable, with substantial token use and inference latency recorded.