RoVi-Aug：面向跨本体机器人学习的机器人与视角增强
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
AI总结：
本文提出 RoVi-Aug，利用图像到图像生成模型合成不同机器人与摄像机视角的演示，以缓解跨本体数据分布不均问题，实现零样本部署并将成功率最高提升30%。
摘要：
扩大机器人学习规模需要庞大且多样化的数据集，而如何高效复用已采集数据并将策略迁移到新的本体，仍是一个尚未解决的问题。Open-X Embodiment（OXE）项目等新兴研究已经表明，通过组合包含不同机器人的数据集来利用技能具有良好前景。然而，许多数据集中机器人类型和摄像机角度的分布不均衡，使策略容易发生过拟合。为缓解这一问题，我们提出 RoVi-Aug，它利用最先进的图像到图像生成模型，通过合成具有不同机器人和摄像机视角的演示来增强机器人数据。通过大量物理实验，我们表明，借助在机器人增强和视角增强数据上的训练，RoVi-Aug 可以零样本部署到摄像机角度显著不同、且未见过的机器人上。与 Mirage 等测试时适应算法相比，RoVi-Aug 在测试时不需要额外处理，不假设摄像机角度已知，并允许策略微调。此外，通过在原始机器人数据集和增强机器人数据集上协同训练，RoVi-Aug 可以学习多机器人、多任务策略，从而实现机器人与技能之间更高效的迁移，并将成功率最高提升 30%。项目网站：https://rovi-aug.github.io。
英文摘要：
Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emerging research such as the Open-X Embodiment (OXE) project has shown promise in leveraging skills by combining datasets including different robots. However, imbalances in the distribution of robot types and camera angles in many datasets make policies prone to overfit. To mitigate this issue, we propose RoVi-Aug, which leverages state-of-the-art image-to-image generative models to augment robot data by synthesizing demonstrations with different robots and camera views. Through extensive physical experiments, we show that, by training on robot- and viewpoint-augmented data, RoVi-Aug can zero-shot deploy on an unseen robot with significantly different camera angles. Compared to test-time adaptation algorithms such as Mirage, RoVi-Aug requires no extra processing at test time, does not assume known camera angles, and allows policy fine-tuning. Moreover, by co-training on both the original and augmented robot datasets, RoVi-Aug can learn multi-robot and multi-task policies, enabling more efficient transfer between robots and skills and improving success rates by up to 30%. Project website: https://rovi-aug.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
