63 citations · 105 across the 4 of their papers we have counts for
6 papers
A Survey on Reinforcement Learning Methods in Character Animation
Ariel Kwiatkowski, Eduardo Alvarado, Vicky Kalogeiton +4
Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environme…
Analysis of emergent patterns in crossing flows of pedestrians reveals an invariant of `stripe' formation in human data
Pratik Mullick, Sylvain Fontaine, Cécile Appert-Rolland +3
When two streams of pedestrians cross at an angle, striped patterns spontaneously emerge as a result of local pedestrian interactions. This clear case of self-organized pattern for…
A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
Beatriz Cabrero Daniel, Ricardo Marques, Ludovic Hoyet +2
Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be…
Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd
Fabien Grzeskowiak, David Gonon, Daniel Dugas +7
The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a be…
Experimental Study of Collective Pedestrian Dynamics
Cécile Appert-Rolland, Julien Pettré, Anne-Hélène Olivier +4
We report on two series of experiments, conducted in the frame of two different collaborations designed to study how pedestrians adapt their trajectories and velocities in groups o…
Vision-based macroscopic pedestrian models
Pierre Degond, Cécile Appert-Rolland, Julien Pettré +1
We propose a hierarchy of kinetic and macroscopic models for a system consisting of a large number of interacting pedestrians. The basic interaction rules are derived from earlier…