Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly
<|reference_start|>Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly: Generalizable long-horizon robotic assembly requires reasoning at multiple levels of abstraction. End-to-end imitation learning (IL) has been proven a promising approach, but it requires a large amount of demonstration data for training and often fails to meet the high-precision requirement of assembly tasks. Reinforcement Learning (RL) approaches have succeeded in high-precision assembly tasks, but suffer from sample inefficiency and hence, are less competent at long-horizon tasks. To address these challenges, we propose a hierarchical modular approach, named ARCH (Adaptive Robotic Composition Hierarchy), which enables long-horizon high-precision assembly in contact-rich settings. ARCH employs a hierarchical planning framework, including a low-level primitive library of continuously parameterized skills and a high-level policy. The low-level primitive library includes essential skills for assembly tasks, such as grasping and inserting. These primitives consist of both RL and model-based controllers. The high-level policy, learned via imitation learning from a handful of demonstrations, selects the appropriate primitive skills and instantiates them with continuous input parameters. We extensively evaluate our approach on a real robot manipulation platform. We show that while trained on a single task, ARCH generalizes well to unseen tasks and outperforms baseline methods in terms of success rate and data efficiency. Videos can be found at https://long-horizon-assembly.github.io.<|reference_end|>
@article{sun2024hierarchical,
    title={Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic
  Assembly},
    author={Jiankai Sun, Aidan Curtis, Yang You, Yan Xu, Michael Koehle, Leonidas
  Guibas, Sachin Chitta, Mac Schwager, Hui Li},
    journal={arXiv preprint arXiv:2409.16451},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16451},
    primaryClass={cs.RO}
}