154 citations · 772 across the 17 of their papers we have counts for
18 papers
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
Jianhao Jiao, Peng Yun, Lei Tai +1
Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the…
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Yongjian Chen, Lei Tai, Kai Sun +1
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Mo…
High-speed Autonomous Drifting with Deep Reinforcement Learning
Peide Cai, Xiaodong Mei, Lei Tai +2
Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics,…
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
Congcong Liu, Yuying Chen, Lei Tai +2
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions. However, there…
Gaze Training by Modulated Dropout Improves Imitation Learning
Yuying Chen, Congcong Liu, Lei Tai +2
Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to hav…
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues
Ting Sun, Lei Tai, Zhihan Gao +2
This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only. The class-specific activation maps from the well-trained classifiers are us…
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
Lei Tai, Peng Yun, Yuying Chen +3
End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually…
Focal Loss in 3D Object Detection
Peng Yun, Lei Tai, Yuan Wang +2
3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larg…
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
Yuan Wang, Tianyue Shi, Peng Yun +2
In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transforme…
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Oleksii Zhelo, Jingwei Zhang, Lei Tai +2
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal ext…
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
Jingwei Zhang, Lei Tai, Peng Yun +4
In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the rea…
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Giuseppe Paolo, Lei Tai, Ming Liu
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep…
Neural SLAM: Learning to Explore with External Memory
Jingwei Zhang, Lei Tai, Ming Liu +2
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimi…
Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention
Qinghai Liao, Ming Liu, Lei Tai +1
Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous se…
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
Lei Tai, Giuseppe Paolo, Ming Liu
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as i…
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
Lei Tai, Jingwei Zhang, Ming Liu +2
Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learnin…
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Lei Tai, Ming Liu
Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent st…
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
Lei Tai, Haoyang Ye, Qiong Ye +1
Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI provid…