Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space

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:this https URL.

@article{yang2025_2505.17389,
  title={ Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space },
  author={ Jinrong Yang and Kexun Chen and Zhuoling Li and Shengkai Wu and Yong Zhao and Liangliang Ren and Wenqiu Luo and Chaohui Shang and Meiyu Zhi and Linfeng Gao and Mingshan Sun and Hui Cheng },
  journal={arXiv preprint arXiv:2505.17389},
  year={ 2025 }
}