32 citations · 72 across the 11 of their papers we have counts for
11 papers
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
Bingqian Lin, Yanxin Long, Yi Zhu +4
Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN…
Surfer: Progressive Reasoning with World Models for Robotic Manipulation
Pengzhen Ren, Kaidong Zhang, Hetao Zheng +5
Considering how to make the model accurately understand and follow natural language instructions and perform actions consistent with world knowledge is a key challenge in robot man…
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…
Contrastive Instruction-Trajectory Learning for Vision-Language Navigation
Xiwen Liang, Fengda Zhu, Yi Zhu +3
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step follo…
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…
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
Fengda Zhu, Xiaojun Chang, Runhao Zeng +1
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a rein…
Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
Fengda Zhu, Linchao Zhu, Yi Yang
There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in th…