Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation (2408.11812v1)

Abstract: Modern machine learning systems rely on large datasets to attain broad generalization, and this often poses a challenge in robot learning, where each robotic platform and task might have only a small dataset. By training a single policy across many different kinds of robots, a robot learning method can leverage much broader and more diverse datasets, which in turn can lead to better generalization and robustness. However, training a single policy on multi-robot data is challenging because robots can have widely varying sensors, actuators, and control frequencies. We propose CrossFormer, a scalable and flexible transformer-based policy that can consume data from any embodiment. We train CrossFormer on the largest and most diverse dataset to date, 900K trajectories across 20 different robot embodiments. We demonstrate that the same network weights can control vastly different robots, including single and dual arm manipulation systems, wheeled robots, quadcopters, and quadrupeds. Unlike prior work, our model does not require manual alignment of the observation or action spaces. Extensive experiments in the real world show that our method matches the performance of specialist policies tailored for each embodiment, while also significantly outperforming the prior state of the art in cross-embodiment learning.

Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion, and Aviation

The paper "Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation" by Ria Doshi, Homer Walke, Oier Mees, Sudeep Dasari, and Sergey Levine presents a novel approach to addressing a central challenge in robot learning—generalization across multiple robotic platforms with varied sensory inputs and control mechanisms. The researchers propose a transformer-based policy, named CrossFormer, and provide thorough empirical validation demonstrating its utility and robustness.

Introduction to Cross-Embodied Robot Learning

Robot learning has traditionally been constrained by the specificity of datasets to individual robots and tasks, posing a challenge for broad generalization. By leveraging data from multiple robotic platforms, CrossFormer aims to enhance generalization and robustness. This approach, however, faces significant challenges due to the heterogeneity of sensors, actuators, and control frequencies among different robots.

CrossFormer: A Transformer-Based Policy

CrossFormer is designed to process data from a diverse range of embodiments, from single and dual-arm manipulation systems to wheeled robots, quadcopters, and quadrupeds, without requiring manual alignment of observation or action spaces. The model architecture employs a transformer backbone that tokenizes and sequences diverse sensor inputs, enabling it to handle varying observation types and predict actions across multiple dimensions. This flexibility is achieved through the tokenization process and a multi-head setup that integrates different action outputs.

Training on a Diverse Dataset

The model was trained on the most extensive and diverse dataset to date, consisting of 900,000 trajectories from 20 different robot embodiments. These datasets include various observation modalities and action spaces, such as front-facing camera views, wrist-mounted cameras, proprioceptive sensors, and diverse control actions.

Key Contributions and Results

The core contributions of the paper are:

1. A transformer-based policy architecture that supports sequence tokenization of observations and actions across varied robotic systems.
2. Empirical evidence demonstrating that the policy can generalize across different embodiments without negative transfer, maintaining performance parity with specialist policies tuned for individual robots.
3. A comprehensive ablation and evaluation showing that CrossFormer outperforms previous state-of-the-art methods in cross-embodiment learning despite the heterogeneity in the datasets.

Experimental results from various robot setups—such as WidowX and Franka manipulation, ALOHA bimanual manipulation, LoCoBot navigation, and Go1 quadrupedal locomotion—revealed that CrossFormer not only matched but often surpassed the performance of specialist policies. Additionally, the model was evaluated in real-world settings, showing significant robustness and adaptability.

Analysis of Transfer Learning

In comparing CrossFormer to the previously established alignment methods, the model demonstrated superior performance without the need for manual alignment of observation and action spaces. This was specifically notable in its ability to handle diverse input sequences and predict actions dynamically, accommodating multiple sensor modalities and control tasks seamlessly.

Implications and Future Work

The implications of this research are profound for both practical and theoretical advancements in AI and robotics. Practically, CrossFormer reduces the necessity for extensive manual engineering across different robotic platforms, enhancing efficiency in developing versatile robotic systems. Theoretically, it provides a foundation for future work in cross-embodiment learning, suggesting potential directions for improving model architectures to facilitate positive transfer across different robots more effectively.

Conclusion

CrossFormer represents a significant advance in the field of robot learning, offering a robust, scalable solution for cross-embodied control. The research validates the effectiveness of a transformer-based approach in generalizing policies across diverse observations and actions, setting a new standard for future developments. While current results are promising, further research with larger datasets and exploration into embedding transfer mechanisms can expand these capabilities, pushing toward more universally applicable robotic control systems. The work opens new avenues for leveraging large multi-robot datasets and optimizing transformer architectures for high-frequency control tasks.