Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space (2505.17389v1)

Abstract: Imitation learning (IL) with human demonstrations is a promising method for robotic manipulation tasks. While minimal demonstrations enable robotic action execution, achieving high success rates and generalization requires high cost, e.g., continuously adding data or incrementally conducting human-in-loop processes with complex hardware/software systems. In this paper, we rethink the state/action space of the data collection pipeline as well as the underlying factors responsible for the prediction of non-robust actions. To this end, we introduce a Hierarchical Data Collection Space (HD-Space) for robotic imitation learning, a simple data collection scheme, endowing the model to train with proactive and high-quality data. Specifically, We segment the fine manipulation task into multiple key atomic tasks from a high-level perspective and design atomic state/action spaces for human demonstrations, aiming to generate robust IL data. We conduct empirical evaluations across two simulated and five real-world long-horizon manipulation tasks and demonstrate that IL policy training with HD-Space-based data can achieve significantly enhanced policy performance. HD-Space allows the use of a small amount of demonstration data to train a more powerful policy, particularly for long-horizon manipulation tasks. We aim for HD-Space to offer insights into optimizing data quality and guiding data scaling. project page: https://hd-space-robotics.github.io.

Overview of HD-Space for Imitation Learning in Robotic Manipulation

The research presented in the paper outlines a novel approach to imitation learning (IL) for robotic manipulation, specifically addressing the challenges posed by long-horizon tasks. This approach, termed Hierarchical Data Collection Space (HD-Space), aims to enhance the efficiency and robustness of data collection, thereby improving the performance of IL-trained policies without the need for large quantities of demonstration data.

Methodological Innovation

The core contribution of this research is the introduction of HD-Space, which optimizes the data collection process for IL by systematically segmenting complex manipulation tasks into atomic subtasks, each with defined state/action spaces. This segmentation allows for the efficient traversal of data collection spaces that are prone to prediction errors, thereby minimizing these errors in policy training. Unlike traditional techniques such as Human-in-the-Loop (HIL) or Real2Sim2Real, HD-Space requires fewer demonstrations and mitigates the compounding errors often observed when trained models deviate from demonstration-tested states.

Empirical Evaluation

The paper conducts empirical evaluations across two simulated and five real-world tasks, demonstrating HD-Space's superior efficacy in improving task success rates and reducing demonstration data costs. Notably, HD-Space enables models to utilize fewer data points per demonstration while achieving improved performance outcomes. This is evidenced by significant gains in success rates across varied manipulation tasks such as grabbing teacups, spoons, and mobile objects in factory settings. Comparatively, HD-Space yielded performance improvements ranging from 8% to 44% over conventional methods, underscoring its effectiveness in tasks requiring dynamic position, angle, and speed generalization.

Practical Implications

The implications of HD-Space are multifaceted. Practically, it facilitates the development of more efficient robotic systems capable of performing long-horizon tasks with reduced human input. Researchers and engineers in robotics can leverage HD-Space for applications requiring sophisticated manipulation, where data collection costs and hardware limitations are a concern. Moreover, HD-Space can be instrumental in advancing robust policies in scenarios with higher error-prone predictions due to unseen states.

Theoretical Contributions

Theoretically, HD-Space offers insights into data space optimization, suggesting that task segmentation can yield higher-quality datasets through targeted exploration of state/action spaces. This enhances the model's ability to maintain reliable performance on more complex or extended sequences of manipulation tasks. The focus on proactive data quality optimization represents a shift from passive error correction strategies typical of current methodologies, providing a fresh perspective for future studies in the field of IL.

Future Directions

While HD-Space's implementation has shown promising results, future work could extend its application to multi-task models or incorporate its principles into Vision-Language-Action (VLA) frameworks. These extensions could potentially enable models to share common conditions such as text descriptions or image goals, further enhancing task generalization abilities. Additionally, exploring HD-Space's integration with advanced model architectures could provide synergistic effects, elevating the capabilities of IL systems even further.

In conclusion, the HD-Space framework marks a significant step forward in imitation learning for robotic manipulation. By optimizing data collection spaces, it reduces reliance on extensive demonstration datasets and paves the way for more efficient, scalable IL systems. As such, it holds promise for both advancing theoretical understandings of IL and enhancing practical robotic applications.