8 citations · 23 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…