Data Scaling Laws in Imitation Learning for Robotic Manipulation (2410.18647v1)

Abstract: Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any environment. To this end, we conduct a comprehensive empirical study on data scaling in imitation learning. By collecting data across numerous environments and objects, we study how a policy's generalization performance changes with the number of training environments, objects, and demonstrations. Throughout our research, we collect over 40,000 demonstrations and execute more than 15,000 real-world robot rollouts under a rigorous evaluation protocol. Our findings reveal several intriguing results: the generalization performance of the policy follows a roughly power-law relationship with the number of environments and objects. The diversity of environments and objects is far more important than the absolute number of demonstrations; once the number of demonstrations per environment or object reaches a certain threshold, additional demonstrations have minimal effect. Based on these insights, we propose an efficient data collection strategy. With four data collectors working for one afternoon, we collect sufficient data to enable the policies for two tasks to achieve approximately 90% success rates in novel environments with unseen objects.

An Analysis of Data Scaling Laws in Imitation Learning for Robotic Manipulation

The paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation" details a comprehensive empirical paper on how data scaling affects the generalization capabilities of robotic manipulation policies trained through imitation learning. It seeks to identify whether scaling laws—a concept that has significantly influenced fields such as NLP and CV—also apply to robotics. The authors aim to evaluate if appropriately scaled data can result in single-task robot policies that generalize effectively across novel objects and environments without additional fine-tuning.

Key Findings and Methodology

The research explores various factors affecting the generalization of robotic policies:

1. Environment and Object Diversity: The paper reveals that the diversity of training environments and objects is notably more critical to policy generalization than the absolute number of demonstrations. The empirical data suggests that the policy’s ability to generalize follows a power-law relationship with the number of training environments and objects.
2. Power-Law Relationships: Experiments indicate that the generalization capacity scales as a power law with the number of training environments and objects. Surprisingly, further increasing the number of demonstrations in a given environment or for a particular object leads to rapidly diminishing returns.
3. Practical Data Collection Strategy: An efficient data collection strategy is proposed based on these findings. The strategy emphasizes collecting data in diverse environments, each with a unique manipulation object. The authors suggest that data from 32 environment-object pairs with around 50 demonstrations each are generally sufficient to train a policy achieving approximately 90% success rates in new scenarios.
4. Implications for Robotic Manipulation: The paper's results imply that deploying robotic manipulation policies in new environments and settings could become feasible with modest data collection efforts. This efficiency paves the way for deploying such policies without extensive fine-tuning, reducing time and resources required for practical applications.

Theoretical and Practical Implications

The identification of power-law relationships in data scaling for robotic manipulation has significant theoretical and practical implications. Theoretically, it provides a framework to predict the generalization potential of robotic policies based on the diversity of training data. Practically, it offers a methodology for efficiently collecting data to train robust policies that require minimal adjustments when transferred to new environments and tasks.

Future Directions

The paper's findings open several avenues for future research:

• Task-Level Generalization: Extending scaling studies to scenarios involving multiple tasks could provide insights into constructing more versatile policies.
• Incorporating Reinforcement Learning: Investigating how reinforcement learning techniques can complement imitation learning to further enhance policy robustness could be beneficial.
• Advanced Data Collection Tools: Developing more sophisticated data collection tools or algorithms to handle limitations like embodiment gaps could enhance data quality.

Conclusion

This paper contributes valuable insights into how the principles of data scaling, previously observed in NLP and CV, apply to imitation learning in robotic manipulation. The findings advocate for the importance of data diversity over sheer volume, offering a path toward efficient training of generalizable robotic policies. Such advancements indicate promising developments towards creating adaptable, zero-shot deployment-ready robotic systems for diverse real-world applications.