Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation

Chengcheng Wang, Yuheng Shi, Siyu Xu, Yutong Wang, Jinjing Zhao, Chenghao Xia, Jinqi Liao, Jiankun Peng, Ligong Bi, Yuechuan Ma, Jiaqi Liu, Wenbo Wang, Zijian Wang, Yuemin Wu, Minjing Dong, Xiu Su, Tao Huang, Jianyuan Guo, Chengbin Du, Yunke Wang, Chang Xu

Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation: 8 upvotes on Hugging Face Daily Papers, #18 of 39 papers on 2026-11-10. Day-by-day upvote history.

Upvotes over time

It had 4 upvotes by its first day, 4 upvotes by the next day, 8 upvotes by day 3, 8 upvotes by day 7, and has 8 now. It ranked #63 of 133 papers in its week and #190 of 340 in its month. Its code repository has 34 GitHub stars.

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, a task-agnostic agent harness that closes 68.3% of this gap in zero-shot manipulation without target-task trials or resets. In Auriga, the model makes all task decisions over an explicit task belief and acts through either direct tool calls or synthesized multi-step Python programs, while the harness supplies calibrated 3D measurements, finger-referenced dual-arm control, and a single admission gateway to the robot. 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 (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

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.