15 citations · 17 across the 5 of their papers we have counts for
5 papers
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph
Xin Ye, Yezhou Yang
We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such…
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling
Xin Ye, Yezhou Yang
Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinfor…
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)
Xin Ye, Yezhou Yang
Visual Indoor Navigation (VIN) task has drawn increasing attention from the data-driven machine learning communities especially with the recently reported success from learning-bas…
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
Xin Ye, Zhe Lin, Joon-Young Lee +3
We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in an indoor environment solely from its visual i…
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots
Xin Ye, Zhe Lin, Haoxiang Li +2
We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-dr…