ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly

Published in CoRL 2025, 2025

ARCH proposes a hierarchical modular approach for long-horizon, high-precision robotic assembly in contact-rich settings. It employs a hierarchical planning framework, including a low-level primitive library of parameterized skills and a high-level policy learned via IL. ARCH generalizes well to unseen objects and outperforms baseline methods in terms of success rate and data efficiency.

Recommended citation: Sun, J., Curtis, A., You, Y., Xu, Y., Koehle, M., Chen, Q., Huang, S., Guibas, L., Chitta, S., Schwager, M., & Li, H. (2025). ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly. CoRL 2025.