18 citations · 43 across the 20 of their papers we have counts for
20 papers
Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation
Keke Tang, Tianyu Hao, Weilong Peng +5
Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing approaches are typically limit…
TLP: A Deep Learning-based Cost Model for Tensor Program Tuning
Yi Zhai, Yu Zhang, Shuo Liu +4
Tensor program tuning is a non-convex objective optimization problem, to which search-based approaches have proven to be effective. At the core of the search-based approaches lies…
MAROAM: Map-based Radar SLAM through Two-step Feature Selection
Dequan Wang, Yifan Duan, Xiaoran Fan +3
In this letter, we propose MAROAM, a millimeter wave radar-based SLAM framework, which employs a two-step feature selection process to build the global consistent map. Specifically…
PFilter: Building Persistent Maps through Feature Filtering for Fast and Accurate LiDAR-based SLAM
Yifan Duan, Jie Peng, Yu Zhang +2
Simultaneous localization and mapping (SLAM) based on laser sensors has been widely adopted by mobile robots and autonomous vehicles. These SLAM systems are required to support acc…
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing
Defeng Xie, Jianmin Ji, Jiafei Xu +1
The dependency tree of a natural language sentence can capture the interactions between semantics and words. However, it is unclear whether those methods which exploit such depende…
A Q-learning Control Method for a Soft Robotic Arm Utilizing Training Data from a Rough Simulator
Peijin Li, Gaotian Wang, Hao Jiang +4
It is challenging to control a soft robot, where reinforcement learning methods have been applied with promising results. However, due to the poor sample efficiency, reinforcement…
Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning
Shunyi Yao1, Guangda Chen, Quecheng Qiu +3
It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like so…
DRQN-based 3D Obstacle Avoidance with a Limited Field of View
Yu'an Chen, Guangda Chen, Lifan Pan +4
In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonom…
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model
Yu'an Chen, Ruosong Ye, Ziyang Tao +7
Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control…
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
Xiaomeng Chu, Jiajun Deng, Yao Li +4
As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual…
3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
Peng Yin, Lingyun Xu, Jianmin Ji +2
One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To…
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
Yingjie Wang, Qiuyu Mao, Hanqi Zhu +5
In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms. Next, we discu…
Neural networks behave as hash encoders: An empirical study
Fengxiang He, Shiye Lei, Jianmin Ji +1
The input space of a neural network with ReLU-like activations is partitioned into multiple linear regions, each corresponding to a specific activation pattern of the included ReLU…
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control
Nan Lin, Yuxuan Li, Yujun Zhu +6
Traditionally, reinforcement learning methods predict the next action based on the current state. However, in many situations, directly applying actions to control systems or robot…
Robot Navigation with Map-Based Deep Reinforcement Learning
Guangda Chen, Lifan Pan, Yu'an Chen +5
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation envi…
MRS-VPR: a multi-resolution sampling based global visual place recognition method
Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen +7
Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving l…
A Multi-Domain Feature Learning Method for Visual Place Recognition
Peng Yin, Lingyun Xu, Xueqian Li +6
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and…
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Shi Yin, Yi Zhou, Chenguang Li +4
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet,…
Well-Founded Operators for Normal Hybrid MKNF Knowledge Bases
Jianmin Ji, Fangfang Liu, Jia-Huai You
Hybrid MKNF knowledge bases have been considered one of the dominant approaches to combining open world ontology languages with closed world rule-based languages. Currently, the on…
Implementing Default and Autoepistemic Logics via the Logic of GK
Jianmin Ji, Hannes Strass
The logic of knowledge and justified assumptions, also known as logic of grounded knowledge (GK), was proposed by Lin and Shoham as a general logic for nonmonotonic reasoning. To d…