Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation
Overview
This paper introduces Auriga, a task-agnostic agent harness designed to bridge the planning-execution gap in zero-shot embodied manipulation. Developed by Chengcheng Wang and colleagues, the system utilizes vision-language models for task decision-making while managing physical execution through calibrated 3D measurements and dual-arm control. The report highlights that Auriga closes 68.3% of the performance gap observed on RoboDojo without requiring target-task trials or resets. By inspecting visual trajectory previews and checking stage tolerances during execution, the harness achieves a 57.7% success rate on RoboDojo and a 73.2% success rate on RoboCasa365, outperforming reference harnesses and VLA-assisted agents in composite tasks.
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Event evidence and corrections
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Reported quantity · Reported success rate: 22.5 percent · Basis not reported · Differing source assertions
“arXiv:2611.01112v1 Announce Type: new Abstract: Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting directly as robot policies. On RoboDojo, a large planning-execution gap separates high-level task decisions from physical execution: removing arm execution raises GPT-6 Astra's success rate from 22.5% under the reference control interface to 76.2%. We present Auriga,”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 76.2 percent · Basis not reported · Differing source assertions
“arXiv:2611.01112v1 Announce Type: new Abstract: Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting directly as robot policies. On RoboDojo, a large planning-execution gap separates high-level task decisions from physical execution: removing arm execution raises GPT-6 Astra's success rate from 22.5% under the reference control interface to 76.2%. We present Auriga,”
Exact source · revision 1Source owner not reported
Reported quantity · Grasp-and-place success rate: 57.7 percent · Basis not reported
“Before motion, the model inspects a visual trajectory preview while simulation is paused; during execution, the harness checks declared stage tolerances and grasp bounds, stopping the robot on divergence and returning the measured discrepancy for in-place recovery. Developed across 13 cross-task capability iterations and frozen for evaluation, Auriga ranks first on the RoboDojo leaderboard with a 57.7% success rate (”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 73.2 percent · Basis not reported · Differing source assertions
“64.0 Score), more than doubling the reference harness on the same model, and remains first under scene randomization while learned policies drop by up to 33.9%. On RoboCasa365, the same frozen harness reaches a 73.2% success rate, outperforming VLA-assisted agents by up to 23.1% on composite tasks. Project page: https://auriga.stelledge.com/ Code: https://github.com/lose4578/Auriga”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“64.0 Score), more than doubling the reference harness on the same model, and remains first under scene randomization while learned policies drop by up to 33.9%. On RoboCasa365, the same frozen harness reaches a 73.2% success rate, outperforming VLA-assisted agents by up to 23.1% on composite tasks. Project page: https://auriga.stelledge.com/ Code: https://github.com/lose4578/Auriga”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAuriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation
This paper introduces Auriga, a task-agnostic agent harness designed to bridge the planning-execution gap in zero-shot embodied manipulation. Developed by Chengcheng Wang and colleagues, the system utilizes vision-language models for task decision-making while managing physical execution through calibrated 3D measurements and dual-arm control. The report highlights that Auriga closes 68.3% of the performance gap observed on RoboDojo without requiring target-task trials or resets. By inspecting visual trajectory previews and checking stage tolerances during execution, the harness achieves a 57.7% success rate on RoboDojo and a 73.2% success rate on RoboCasa365, outperforming reference harnesses and VLA-assisted agents in composite tasks.
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