# Zero-Shot Manipulation

Vision-language models can read a task yet still fumble the grasp: on RoboDojo, removing arm execution raises the same model's success rate from 22.5% to 76.2%, locating the bottleneck in physical execution. Auriga separates model decisions from robot execution, with the model keeping an explicit task belief and every motion passing a single admission gateway that previews the trajectory, checks stage tolerances during execution, and stops on divergence. Frozen for evaluation, Auriga reaches 57.7% success and 64.0 Score across 42 tasks, ranking first, and 73.2% on RoboCasa365.

Vision-language models can read a task yet still fumble the grasp: on RoboDojo, removing arm execution raises the same model's success rate from 22.5% to 76.2%, locating the bottleneck in physical execution. Auriga separates model decisions from robot execution, with the model keeping an explicit task belief and every motion passing a single admission gateway that previews the trajectory, checks stage tolerances during execution, and stops on divergence. Frozen for evaluation, Auriga reaches 57.7% success and 64.0 Score across 42 tasks, ranking first, and 73.2% on RoboCasa365.

A pretrained vision-language model can act without robot-action pretraining once a tool-center-point action frame is projected into the wrist-camera view and actions are emitted through its native text interface, reaching 79.4% average zero-shot success on simple manipulation tasks, close to VLA models fine-tuned with about 30 demonstrations.

A pretrained vision-language model can act without robot-action pretraining once a tool-center-point action frame is projected into the wrist-camera view and actions are emitted through its native text interface, reaching 79.4% average zero-shot success on simple manipulation tasks, close to VLA models fine-tuned with about 30 demonstrations.