activity
20192021
most citedMobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

15 citations · 25 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO20216 cited

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts

Amanda Prorok, Jan Blumenkamp, Qingbiao Li +3

Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of le…

eess.IV2020

Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution

Ruoxi Wang, Dandan Zhang, Qingbiao Li +2

In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's…

cs.CV2020

Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

Weizhe Lin, Indigo Orton, Qingbiao Li +2

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to moda…

cs.RO202015 cited

Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

Binyu Wang, Zhe Liu, Qingbiao Li +1

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoid…

cs.CL2020

Text Classification with Lexicon from PreAttention Mechanism

QingBiao LI, Chunhua Wu, Kangfeng Zheng

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. And it improves the utilization of the linguistic knowledge. Although i…

cs.CL20204 cited

Hierarchical Transformer Network for Utterance-level Emotion Recognition

QingBiao Li, ChunHua Wu, KangFeng Zheng +1

While there have been significant advances in de-tecting emotions in text, in the field of utter-ance-level emotion recognition (ULER), there are still many problems to be solved.…