Human motion trajectory prediction: A survey
With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …
of such systems to perceive, understand, and anticipate human behavior becomes …
Core challenges of social robot navigation: A survey
Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …
variety of engineering and human factors challenges. These challenges have motivated a …
Trace and pace: Controllable pedestrian animation via guided trajectory diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body
animations that can be controlled to meet user-defined goals. We draw on recent advances …
animations that can be controlled to meet user-defined goals. We draw on recent advances …
Human trajectory prediction via neural social physics
Trajectory prediction has been widely pursued in many fields, and many model-based and
model-free methods have been explored. The former include rule-based, geometric or …
model-free methods have been explored. The former include rule-based, geometric or …
Human trajectory forecasting in crowds: A deep learning perspective
Since the past few decades, human trajectory forecasting has been a field of active research
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
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 …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
Motion planning among dynamic, decision-making agents with deep reinforcement learning
Robots that navigate among pedestrians use collision avoidance algorithms to enable safe
and efficient operation. Recent works present deep reinforcement learning as a framework …
and efficient operation. Recent works present deep reinforcement learning as a framework …
A review of motion planning algorithms for intelligent robots
Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …
These algorithms include traditional planning algorithms, classical machine learning …
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios
Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …
in the decentralized scenarios where each robot generates its paths with limited observation …