🤖 AI Summary
Robot learning suffers from fragmentation, requiring separate model training for each robot platform, task, and environment.
Method: This paper introduces X-robot, a universal policy paradigm enabling the first cross-platform positive transfer. We construct the largest standardized robotic manipulation dataset to date, developed collaboratively across multiple institutions. We propose RT-X, a Transformer-based architecture that unifies action representations and cross-robot data formats, trained via large-scale multi-robot behavioral cloning and transfer learning.
Contribution/Results: RT-X demonstrates significant skill generalization across 22 heterogeneous robot platforms. In zero-shot and few-shot settings on unseen robots, it achieves substantial improvements in task success rates. This work provides the first systematic empirical validation of both the effectiveness and scalability of cross-robot positive transfer—establishing a foundational step toward generalizable robotic policies.
📝 Abstract
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io.
