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RT-1: Robotics Transformer for Real-World Control at Scale (2212.06817v2)

Published 13 Dec 2022 in cs.RO, cs.AI, cs.CL, cs.CV, and cs.LG

Abstract: By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer1.github.io

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RT-1: Robotics Transformer for Real-World Control at Scale

The paper presents a novel approach in robotic learning through the development of a model known as Robotics Transformer 1 (RT-1). This research targets the challenge of enhancing robot learning capabilities to manage a vast array of real-world tasks. The proposed RT-1 model leverages the ability to process large and diverse datasets to facilitate multi-task learning with significant scalability.

Overview and Methodology

RT-1 uses a Transformer-based architecture to absorb data and learn from a diverse set of robotic tasks. The model processes input through an architecture consisting of a FiLM-conditioned EfficientNet, TokenLearner, and Transformer. It uses a tokenization strategy for both vision and language inputs, which allows it to manage sequences and produce appropriate robotic actions. This framework enables RT-1 to interpret images and instructions effectively, producing a corresponding series of actions.

The model adheres to a task-agnostic training paradigm, utilizing over 130,000 demonstration episodes gathered from multiple robots over 17 months, covering more than 700 distinct task instructions. The training procedure emphasizes multi-task learning, ensuring that RT-1 can efficiently learn from richly varied datasets presented in realistic environments.

Key Findings

RT-1 demonstrates strong capability in performing a wide array of tasks with a reported success rate of 97% on training instructions. The model exhibits significant robustness against distractors and can generalize to new tasks, environments, and object configurations. Such performance metrics underscore the potential RT-1 has in managing complex scenarios, providing a flexible solution to robot learning.

Notably, RT-1's architecture enables it to utilize heterogeneous data from simulation or different robot morphologies, expanding its learning capacity without compromising the performance on standard tasks. Such adaptability could propel efforts in creating versatile robotic systems capable of operating across diverse settings and utilizing various data sources, such as simulation data for unseen tasks.

Implications

The results suggest promising directions in the field of enhanced robotic autonomy, implying potential utility in various sectors—such as automation and service robotics—where robots are required to handle an assortment of tasks efficiently. Moreover, RT-1's ability to effectively leverage cross-domain knowledge from simulation data introduces possibilities for reducing the cost and effort of large-scale data collection in the real world.

Future Directions

The paper proposes that future advancements may focus on extending the model's capabilities to more dexterous and diverse tasks. Exploration into robust methods that support dynamic and interactive learning environments could further enhance robotic decision-making and action selection mechanisms. Additionally, the potential of incorporating other modalities and enhancing temporal awareness poses an exciting avenue for future research.

Concluding Remarks

RT-1 presents an insightful contribution to robotic learning, showcasing the substantial impact of Transformer-based architectures in absorbing and generalizing knowledge from extensive and varied data sources. This work opens up pathways for future research aiming to design scalable robotic systems capable of versatile and adaptive operation in real-world environments.

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Authors (51)
  1. Anthony Brohan (8 papers)
  2. Noah Brown (10 papers)
  3. Justice Carbajal (2 papers)
  4. Yevgen Chebotar (28 papers)
  5. Joseph Dabis (1 paper)
  6. Chelsea Finn (264 papers)
  7. Keerthana Gopalakrishnan (14 papers)
  8. Karol Hausman (56 papers)
  9. Alex Herzog (4 papers)
  10. Jasmine Hsu (12 papers)
  11. Julian Ibarz (26 papers)
  12. Brian Ichter (52 papers)
  13. Alex Irpan (23 papers)
  14. Tomas Jackson (4 papers)
  15. Sally Jesmonth (2 papers)
  16. Nikhil J Joshi (6 papers)
  17. Ryan Julian (16 papers)
  18. Dmitry Kalashnikov (34 papers)
  19. Yuheng Kuang (8 papers)
  20. Isabel Leal (11 papers)
Citations (761)
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