# 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.

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.

The authors introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive composition with selective observation, letting the agent compose classical robot primitives and learned vision-language-action (VLA) policies into Python cells that perform conditional checks and local retries and return only explicitly requested images and state feedback for replanning; across 700 simulated task instances from LIBERO-PRO, RoboTwin 2.0, and RoboCasa365, compared with a tool-calling baseline using the same GPT-6 Astra planner and the same underlying robot primitives, it raises overall success from 63.1% to 71.7% under equal LLM-call budgets and, on instances solved by both agents, uses 49% fewer LLM calls and 65% fewer input tokens.

The authors introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive composition with selective observation, letting the agent compose classical robot primitives and learned vision-language-action (VLA) policies into Python cells that perform conditional checks and local retries and return only explicitly requested images and state feedback for replanning; across 700 simulated task instances from LIBERO-PRO, RoboTwin 2.0, and RoboCasa365, compared with a tool-calling baseline using the same GPT-6 Astra planner and the same underlying robot primitives, it raises overall success from 63.1% to 71.7% under equal LLM-call budgets and, on instances solved by both agents, uses 49% fewer LLM calls and 65% fewer input tokens.