Xuanhui Xu

2 accepted papers

2023

GAN-Based Editable Movement Primitive From High-Variance Demonstrations

Xuanhui Xu, Mingyu You, Hongjun Zhou, Zhifeng Qian, Weisheng Xu, Bin He

RA-L 2023

Movement Primitive (MP) is a promising Learning from Demonstration (LfD) framework, which is commonly used to learn movements from human demonstrations and adapt the learned movements to new task scenes. A major goal of MP research is to improve the adaptability of MP to various target positions and

Cited by 4 Source ↗Scholar ↗

2023

Goal-Conditioned Reinforcement Learning With Disentanglement-Based Reachability Planning

Zhifeng Qian, Mingyu You, Hongjun Zhou, Xuanhui Xu, Bin He

RA-L 2023

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem b

Cited by 6 Source ↗Scholar ↗