Hierarchical Human-to-Robot Imitation Learning for Long-Horizon Tasks via Cross-Domain Skill Alignment - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Zhenyang Lin, Yurou Chen, Zhiyong Liu 0001. Hierarchical Human-to-Robot Imitation Learning for Long-Horizon Tasks via Cross-Domain Skill Alignment. In IEEE International Conference on Robotics and Automation, ICRA 2024, Yokohama, Japan, May 13-17, 2024. pages 2783-2790, IEEE, 2024. [doi]

The following publications are possibly variants of this publication:

Curriculum-guided skill learning for long-horizon robot manipulation tasksJoão Bernardo Alves 0001, Nuno Lau, Filipe Silva 0001. ras, 192:105032, 2025. [doi]

LeSkill: Structured Skill Learning for Long-Horizon Robotic Manipulation TasksXiucai Huang, Shifeng Chen, Yongduan Song. tsmc, 55(12):8794-8803, December 2025. [doi]

Neural ODE-based Imitation Learning (NODE-IL): Data-Efficient Imitation Learning for Long-Horizon Multi-Skill Robot ManipulationShiyao Zhao, Yucheng Xu, Mohammadreza Kasaei 0001, Mohsen Khadem, Zhibin Li 0001. iros 2024: 8524-8530 [doi]

AutoSkill: Hierarchical Open-Ended Skill Acquisition for Long-Horizon Manipulation Tasks via Language-Modulated RewardsZhenyang Lin, Yurou Chen, Zhiyong Liu 0001. tamd, 17(5):1141-1152, October 2025. [doi]

Skill Learning for Long-Horizon Sequential TasksJoão Alves 0001, Nuno Lau, Filipe Silva. epia 2022: 713-724 [doi]

runs on WebDSL