4 citations · 7 across the 10 of their papers we have counts for
10 papers
D3D-GEN: Robot-Aware Domain-Grounded Interactive 3D World Generation for Social Robotics
Anh Duc Do, Volodymyr Shcherbyna, Tai Duc Nguyen +5
Training and validation of Embodied AI for social navigation critically depends on realistic simulation environments, yet many current approaches fail to find a balance between rea…
GROVE: Grounded Pedestrian Simulation via Natural Language for Interactive Social Robot Navigation
Duc Tai Nguyen, Volodymyr Shcherbyna, Anh Do Duc +3
Pedestrian simulation is a critical component for training and deploying social robot navigation approaches, yet it remains a largely rigid system that repeatedly requires manual d…
HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation
Zhengcheng Shen, Yi Gao, Linh Kästner +1
The advancement of computer vision and machine learning has made datasets a crucial element for further research and applications. However, the creation and development of robots w…
Imagination-augmented Navigation Based on 2D Laser Sensor Observations
Zhengcheng Shen, Linh Kästner, Magdalena Yordanova +1
Autonomous navigation of mobile robots is an essential task for various industries. Sensor data is crucial to ensure safe and reliable navigation. However, sensor observations are…
Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots
Linh Kästner, Bassel Fatloun, Zhengcheng Shen +2
Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment…
Obstacle-aware Waypoint Generation for Long-range Guidance of Deep-Reinforcement-Learning-based Navigation Approaches
Linh Kästner, Xinlin Zhao, Zhengcheng Shen +1
Navigation of mobile robots within crowded environments is an essential task in various use cases, such as delivery, health care, or logistics. Deep Reinforcement Learning (DRL) em…
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation
Linh Kästner, Junhui Li, Zhengcheng Shen +1
Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emer…
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or ine…
Spatial Imagination With Semantic Cognition for Mobile Robots
Zhengcheng Shen, Linh Kästner, Jens Lambrecht
The imagination of the surrounding environment based on experience and semantic cognition has great potential to extend the limited observations and provide more information for ma…
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace…