Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets (2109.13396v1)

Abstract: Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to utilize shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning? We hypothesize that the key is to use datasets with multiple tasks and multiple domains, such that a new user that wants to train their robot to perform a new task in a new domain can include this dataset in their training process and benefit from cross-task and cross-domain generalization. To evaluate this hypothesis, we collect a large multi-domain and multi-task dataset, with 7,200 demonstrations constituting 71 tasks across 10 environments, and empirically study how this data can improve the learning of new tasks in new environments. We find that jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone. We also find that data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains. These results suggest that reusing diverse multi-task and multi-domain datasets, including our open-source dataset, may pave the way for broader robot generalization, eliminating the need to re-collect data for each new robot learning project.

Overview of "Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets"

The paper "Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets" addresses a significant challenge in the domain of robotic learning; namely, the broad generalization of policies across diverse tasks and environments. The paper presented posits that leveraging cross-domain datasets can enhance a robot's ability to adapt to new tasks unseen during its initial training. The researchers examine the potential for reusing datasets, drawing parallels to successful practices in fields such as computer vision, where datasets like ImageNet facilitate broader generalization.

Main Contributions

The work introduces a novel dataset comprising 7,200 demonstrations across 71 tasks within 10 different environments. This dataset is engineered to act as a ‘bridge’, helping robots generalize tasks without the need for extensive new data collections each time a new robot or setting is introduced. This endeavour aligns similar undertakings in other AI areas, such as vision and NLP, with the novel contribution being its application and validation in the robotics context.

The methodology evaluated the bridge dataset's capacity to enhance skill learning when combined with a nominal set of demonstrations from a new domain. Through empirical studies, the authors observed that joint training using the bridge dataset, alongside 50 demonstrations of a new task and domain, yielded a remarkable 2x improvement in success rate compared to using only target domain data.

Methodology and Experimental Insights

The experiments are designed around three core scenarios demonstrating the utility of bridge data:

1. Transfer with Matching Behaviors: Here, tasks included in both bridge and target datasets exhibited improved performance when jointly trained, showcasing the efficacy of the bridge dataset in scenarios akin to domain adaptation.
2. Zero-Shot Transfer with Target Support: A notable outcome of this scenario is the reusability of data, allowing tasks only observed in the bridge data to seamlessly perform in the target domain without new demonstrations, merely through the provision of few supportive tasks.
3. Boosting Generalization of New Tasks: This scenario underscores the potential of bridge data to enhance new task performance, suggesting a broader structure that can adapt novel skills using structural similarities and diversity in the bridge dataset.

These scenarios collectively illustrate the potent role a well-structured bridge dataset can play in expanding robotic capabilities, transcending individual task-specific limitations.

Implications and Future Directions

The implications of this research are vast, indicating a shift towards shared, versatile datasets that could significantly reduce the overhead associated with data collection in robotic learning. The authors speculate on the potential for such datasets to foster collaboration and data-sharing across labs, advancing the field of robotics through shared resources and learning architectures.

Future research may delve into expanding the breadth of the bridge dataset to encompass even more tasks and domains, as well as integrating different types of robots. It might also explore more sophisticated methods for integrating bridge data and target data, enhancing the synergy between diverse datasets.

Conclusion

In conclusion, the paper "Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets" presents a compelling case for the strategic reuse of datasets to enhance robotic generalization capabilities. With empirical evidence substantiating a marked improvement through the use of bridge datasets, this research lays the groundwork for a transformative approach in robotic learning paradigms, facilitating greater adaptability and skill transferability in robotic systems.