no code implementations • 29 Feb 2024 • Yu Zhang, long wen, Xiangtong Yao, Zhenshan Bing, Linghuan Kong, wei he, Alois Knoll
Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from new samples, in conjunction with the learned hyperparameters.
no code implementations • 26 Feb 2024 • Yu Zhang, Guangyao Tian, long wen, Xiangtong Yao, Liding Zhang, Zhenshan Bing, wei he, Alois Knoll
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles.
no code implementations • 30 May 2023 • Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao, Xiaojie Su, Chenguang Yang, Kai Huang, Alois Knoll
In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world.
no code implementations • 29 Apr 2023 • Mingyang Wang, Zhenshan Bing, Xiangtong Yao, Shuai Wang, Hang Su, Chenguang Yang, Kai Huang, Alois Knoll
On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
no code implementations • 21 Sep 2022 • Xiangtong Yao, Zhenshan Bing, Genghang Zhuang, KeJia Chen, Hongkuan Zhou, Kai Huang, Alois Knoll
We propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetrical behaviors and language instructions.