32 citations · 46 across the 6 of their papers we have counts for
8 papers
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
Xiwen Liang, Fengda Zhu, Lingling Li +2
Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. To address this problem, previous works have proposed some methods of fi…
Vision-Language Navigation with Random Environmental Mixup
Chong Liu, Fengda Zhu, Xiaojun Chang +3
Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Large da…
SOON: Scenario Oriented Object Navigation with Graph-based Exploration
Fengda Zhu, Xiwen Liang, Yi Zhu +2
The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual…
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Siyi Hu, Fengda Zhu, Xiaojun Chang +1
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model…
Vision-Dialog Navigation by Exploring Cross-modal Memory
Yi Zhu, Fengda Zhu, Zhaohuan Zhan +4
Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help w…
Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
Fengda Zhu, Yi Zhu, Xiaojun Chang +1
Vision-Language Navigation (VLN) is a task where agents learn to navigate following natural language instructions. The key to this task is to perceive both the visual scene and nat…