RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Abstract: The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and language, we propose a multi-embodiment, multi-task generalist agent for robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned decision transformer capable of consuming action-labelled visual experience. This data spans a large repertoire of motor control skills from simulated and real robotic arms with varying sets of observations and actions. With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task. We also show how a trained model itself can be used to generate data for subsequent training iterations, thus providing a basic building block for an autonomous improvement loop. We investigate the agent's capabilities, with large-scale evaluations both in simulation and on three different real robot embodiments. We find that as we grow and diversify its training data, RoboCat not only shows signs of cross-task transfer, but also becomes more efficient at adapting to new tasks.

• The paper introduces RoboCat, a novel generalist agent that leverages multi-task transformer learning and a self-improvement loop to excel in robotic manipulation.
• The paper employs a vision-based goal-conditioned decision transformer that repurposes diverse data, achieving efficient cross-task transfer with only 100 to 1000 demonstrations.
• Experimental results in simulation and real-world settings demonstrate RoboCat's ability to learn unseen tasks across various robot embodiments while reducing manual dataset preparation.

An Overview of RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

The paper presents RoboCat, a novel approach to robotic manipulation that leverages the strengths of multi-task learning and self-improvement in a generalist framework. RoboCat is built upon the transformer architecture, specifically tailored to handle diverse robotic tasks across various embodiments without requiring common action or observation representations. This paper explores the construction, capabilities, and implications of RoboCat as a scalable, adaptable solution in the field of robotic manipulation.

Key Contributions and Methodology

1. Data and Learning Strategy:
   • RoboCat is trained on a large, heterogeneous dataset comprised of simulations and real-world tasks involving a variety of robotic arms. The dataset is notable for its diversity in motor skill requirements, control frequencies, and robot embodiments.
   • The agent uses a vision-based goal-conditioned decision transformer to interpret action-labeled visual experiences. Such an approach eliminates the dependence on human supervision beyond initial goal setup and can repurpose suboptimal data with foresight goals.
2. Adaptive and Transfer Capabilities:
   • RoboCat demonstrates impressive generalization abilities, with the capability to perform tasks and adapt to new robots with minimal direct training examples (ranging from 100 to 1000 demonstrations).
   • Its self-improvement loop allows the agent to autonomously improve over time by generating its own training data.
3. Experimental Framework:
   • The agent's capacity was thoroughly evaluated, showing substantial cross-task transfer and the ability to learn unseen tasks, including those on different robot embodiments, with significant efficiency.
   • The paper provides extensive empirical results in simulation and real-world settings, with tasks ranging from simple stacking to more complex scenarios involving insertion and precise manipulation.

Results and Implications

RoboCat's quantitative results underscore its efficiency in adapting to new tasks and improving through self-directed learning. The results indicate that as the training data diversifies, RoboCat gains not only in performance across known tasks but also in its adaptability to novel situations. This aspect could fundamentally lower the cost of developing new robotic skills and integrating novel robotic configurations.

The self-improvement aspect of RoboCat signifies a critical shift towards autonomous robotic systems that could iteratively enhance their capabilities. By diminishing the extensive manual dataset preparation typically required in robotics, RoboCat paves the way for more scalable and cost-effective robotics solutions.

Future Directions

The paper suggests several avenues for future exploration:

• Task Specification: Expanding beyond visual goal conditioning to potentially include language-based or multi-modal task definitions could enhance RoboCat's flexibility.
• Reinforcement Learning Integration: While current capabilities are based on imitation learning, incorporating reinforcement learning strategies could enable more sophisticated decision-making and adaptability in dynamic environments.
• System Robustness: Extending RoboCat's application to more diverse, unstructured environments could further validate its robustness and applicability in real-world scenarios.

Conclusion

RoboCat encapsulates a significant advancement in robotic manipulation, rooted in self-improving, data-efficient learning. Its design and results illustrate the potential for foundation models in robotics, similar to those that have transformed fields such as computer vision and natural language processing. The implications of RoboCat for robotics are broad, suggesting a future where robots can continuously learn and adapt with minimal human intervention, thereby enhancing their utility and accessibility across various domains.