外观
论文arXiv 2609.3816429 天前
Rho：高效可适应 VLA 模型的基础
Rho面向双臂操作，用开放权重VLA与具身中期训练提升少数据适应，并在仿真和实体机器人上验证。
Franka FR3原文链接：Rho: A Foundation for Efficiently Adaptable VLA Models（arXiv 2609.38164 · 发布日期：2026-09-29）
Rho: A Foundation for Efficiently Adaptable VLA Models
Rho面向双臂操作，用开放权重VLA与具身中期训练提升少数据适应，并在仿真和实体机器人上验证。
核心要点
- 通用物理AI需兼具视觉语言能力、跨具身精确控制，并能高效适应下游任务。
- Rho采用动作专家架构与训练配方，面向三种双臂机器人具身进行少数据任务适应。
- 消融和仿真、实体实验显示，具身中期训练改善下游适应，整体表现匹配或优于开放权重VLA。
- 其在线适应通过潜在策略学习纠正反馈；作者发布基础模型和具身特定检查点。
验证范围与局限：验证范围为受控仿真和实体机器人实验，覆盖YAM Box、UR AI Trainer、FR3 Duo三种具身及报告中的任务和基线。
英文摘要 (Abstract)
General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient adaptation to downstream tasks. We introduce Rho, a family of open-weights VLA models for bimanual manipulation designed for data-light task adaptation on 3 embodiments representative of dual-arm robots across research labs and the industry -- YAM Box, UR AI Trainer, and FR3 Duo. We systematically ablate Rho's action-expert architecture and training recipe, and show in controlled simulation and physical-robot experiments that embodiment midtraining improves downstream adaptation. The resulting Rho variants for YAM Box, UR AI Trainer, and FR3 Duo match or outperform existing open-weights VLAs and achieve the strongest overall performance across the tasks, embodiments, and baselines evaluated in this report. We further demonstrate the Rho model family's built-in capacity for online adaptation: an internal latent policy learns from corrective feedback to select observation-conditioned noise inputs for the frozen flow-matching action expert. With as few as 15 corrected episodes, adapting this lightweight module enables Rho to handle task situations at the fringe of its offline finetuning distribution. Together, these results position Rho as both a strong general-purpose robotic manipulation model and a practical foundation for adaptation. We release the base Rho model and the embodiment-specific checkpoints to facilitate Rho's deployment in research experiments and practical industrial use cases.
作者：Rho Team, Simran Bagaria, Daphne Chen, Dean Fortier, Jianlong Fu, Michael Harrison, Tess Hellebrekers, Neel Joshi, Andrey Kolobov, Dalton Moore
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