L2b: Learning to balance the safety-efficiency trade-off in interactive crowd-aware robot navigation
M Nishimura, R Yonetani - 2020 IEEE/RSJ International …, 2020 - ieeexplore.ieee.org
This work presents a deep reinforcement learning framework for interactive navigation in a
crowded place. Our proposed Learning to Balance (L2B) framework enables mobile robot …
crowded place. Our proposed Learning to Balance (L2B) framework enables mobile robot …
Crowd-aware robot navigation for pedestrians with multiple collision avoidance strategies via map-based deep reinforcement learning
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 social …
usually assume that pedestrians follow a predefined collision avoidance strategy, like social …
Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning
Mobility in an effective and socially-compliant manner is an essential yet challenging task for
robots operating in crowded spaces. Recent works have shown the power of deep …
robots operating in crowded spaces. Recent works have shown the power of deep …
Crowd-Aware Socially Compliant Robot Navigation via Deep Reinforcement Learning
B Xue, M Gao, C Wang, Y Cheng, F Zhou - International Journal of Social …, 2024 - Springer
Navigating in crowd environments is challenging for mobile robots because not only the
safety but also the comfort of surrounding pedestrians must be considered. In this work, a …
safety but also the comfort of surrounding pedestrians must be considered. In this work, a …
Decentralized structural-rnn for robot crowd navigation with deep reinforcement learning
Safe and efficient navigation through human crowds is an essential capability for mobile
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …
Robot navigation in a crowd by integrating deep reinforcement learning and online planning
Navigating mobile robots along time-efficient and collision-free paths in crowds is still an
open and challenging problem. The key is to build a profound understanding of the crowd …
open and challenging problem. The key is to build a profound understanding of the crowd …
Intention aware robot crowd navigation with attention-based interaction graph
We study the problem of safe and intention-aware robot navigation in dense and interactive
crowds. Most previous reinforcement learning (RL) based methods fail to consider different …
crowds. Most previous reinforcement learning (RL) based methods fail to consider different …
Dynamically feasible deep reinforcement learning policy for robot navigation in dense mobile crowds
U Patel, N Kumar, AJ Sathyamoorthy… - arXiv preprint arXiv …, 2020 - arxiv.org
We present a novel Deep Reinforcement Learning (DRL) based policy to compute
dynamically feasible and spatially aware velocities for a robot navigating among mobile …
dynamically feasible and spatially aware velocities for a robot navigating among mobile …
Socially aware crowd navigation with multimodal pedestrian trajectory prediction for autonomous vehicles
Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …
very challenging task. This is because human movement and interactions are very hard to …
Socially aware robot navigation in crowds via deep reinforcement learning with resilient reward functions
X Lu, H Woo, A Faragasso, A Yamashita… - Advanced …, 2022 - Taylor & Francis
Robots navigating in a robot–human coexisting environment need to optimize their paths not
only for task-related performance (eg safety and efficiency) but also for their social …
only for task-related performance (eg safety and efficiency) but also for their social …