activity
20192021
most citedHierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

6 citations · 13 across the 6 of their papers we have counts for

collaborators

7 papers

cs.RO20213 cited

A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving

Jianbang Liu, Xinyu Mao, Yuqi Fang +2

With the rapid development of machine learning, autonomous driving has become a hot issue, making urgent demands for more intelligent perception and planning systems. Self-driving…

cs.RO20211 cited

Learning-based Fast Path Planning in Complex Environments

Jianbang Liu, Baopu Li, Tingguang Li +3

In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly fin…

cs.RO20216 cited

Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

Fan Bai, Fei Meng, Jianbang Liu +2

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping. It needs…

cs.CV2021

A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Jianbang Liu, Yuqi Fang, Delong Zhu +3

Human activities are hugely restricted by COVID-19, recently. Robots that can conduct inter-floor navigation attract much public attention, since they can substitute human workers…

cs.RO2020

Conditional Generative Adversarial Networks for Optimal Path Planning

Nachuan Ma, Jiankun Wang, Max Q. -H. Meng

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of optimal collision-free path ar…

cs.RO20203 cited

Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments

Jiahui Lin, Tong Zhou, Delong Zhu +2

This paper presents a search-based partial motion planner to generate dynamically feasible trajectories for car-like robots in highly dynamic environments. The planner searches for…