将RT-1-X模型迁移到未训练过的SCARA机器人，发现需微调才能完成抓取任务。
Bringing the RT-1-X Foundation Model to a SCARA robot
- 用演示微调使模型适应新机器人类型
- 成功实现基础技能迁移但不包含物体特异性知识
- 验证了机器人类型泛化需额外训练
传统机器人系统需为每项任务、环境和机器人形态单独训练。尽管机器学习的进步使模型能跨任务和环境泛化，但将其适配到全新设置的挑战仍基本未被探索。本研究考察了RT-1-X机器人基础模型在未见过的机器人类型——来自UMI-RTX的SCARA机器人上的泛化能力。初始实验表明，RT-1-X无法零样本泛化到该新型机器人。然而，通过示范微调，模型可学会原本属于基础模型但曾用于其他机器人类型的抓取任务。当机器人面对基础模型中包含但未出现在微调数据集中的物体时，仅技能被迁移，而物体特定知识未被继承。
原文摘要 · Abstract (English)
Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX. Initial experiments reveal that RT-1-X does not generalize zero-shot to the unseen type of robot. However, fine-tuning of the RT-1-X model by demonstration allows the robot to learn a pickup task which was part of the foundation model (but learned for another type of robot). When the robot is presented with an object that is included in the foundation model but not in the fine-tuning dataset, it demonstrates that only the skill, but not the object-specific knowledge, has been transferred.
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