Glen Berseth

I am an assistant professor at the University de Montreal and Mila. My research explores how to use deep learning and reinforcement learning to develop generalist robots.

#CrowdSimulation Articles


Using synthetic crowds to inform building pillar placements

Brandon Haworth, Muhammad Usman, Glen Berseth, Mahyar Khayatkhoei, Mubbasir Turab Kapadia, Petros Faloutsos

We present a preliminary exploration of synthetic crowds towards computational tools for informing the design of environments (e.g., building floor plans). Feedback and automatic design processes are developed from exploring crowd behaviours and metrics derived from simulations of environments in density stressed scenarios, such as evacuations. Computational approaches for crowd analysis and environment design benefit from measures characterizing the relationships between environments and crowd flow behaviours. We investigate the optimization of environment elements to maximize crowd flow, under a range of LoS conditions, a standard indicator for characterizing the service afforded by environments to crowds of specific densities widely used in crowd management and urban design. The steering algorithm, the number of optimized environment elements, the scenario configuration and the LoS conditions affect the optimal configuration of environment elements. From the insights gained exploring optimizations under LoS conditions, we take steps towards user-in-the-loop optimization and design of an environment by applying an adaptive refinement approach to reduce the search space of the optimization. We derive the fitness of architectural layouts from background simulations. We perform a ground truth study to gauge the performance and quality of our method.


Evaluating and Optimizing Level of Service for Crowd Evacuations

Brandon Haworth, Muhammad Usman, Glen Berseth, Mubbasir Turab Kapadia, Petros Faloutsos

Computational approaches for crowd analysis and environment design need robust measures for characterizing the relation between environments and crowd flow. Level of service (Level of Service) is a standard indicator for characterizing the service afforded by environments to crowds of specific densities, and is widely used in crowd management and urban design. However, current Level of Service indicators are qualitative and rely on expert analysis. In this paper, we perform a systematic analysis of Level of Service for synthetic crowds. The flow-density relationships in crowd evacuation scenarios are explored with respect to three state-of-the-art steering algorithms. Our results reveal that Level of Service is sensitive to the crowd behavior, and the configuration of the environment benchmark. Following this study, we automatically optimize environment elements to maximize crowd flow, under a range of Level of Service conditions. The steering algorithm, the number of optimized environment elements, the scenario configuration and the Level of Service conditions affect the optimal configuration of environment elements. We observe that the critical density of crowd simulators increases due to the optimal placement of pillars, thereby effectively increasing the Level of Service of environments at higher crowd densities. Depending on the simulation technique and environment benchmark, pillars are configured to produce lanes or form wall-like structures, in an effort to maximize crowd flow. These experiments serve as an important precursor to computational crowd optimization and management and motivate the need for further study using additional real and synthetic crowd datasets across a larger representation of environment benchmarks.


Environment Optimization for Crowd Evacuation

Glen Berseth

In this work we parameterize subspaces of the full environment configuration space and optimize parts of these subspaces for an agent flow metric. We focus on building configuration and optimize the possible placement of support pillars or obstacles in the environment.



Robust Space-time Footsteps for Agent-based Steering

Glen Berseth

Randomized, continuous footstep action space.

Geometric validation to ensure valid configuration.

With this work we build upon prevous work to inductavely design a robust footsteps-based steering algorithm. The steering algorithm uses an A* algorithm to generate least cost footstep plans between goals while avoiding dynamic collisions.


SteerFit

Glen Berseth, Brandon Haworth, Mubbasir Turab Kapadia, Petros Faloutsos

In the context of crowd simulation, there is a diverse set of algorithms that model steering. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This paper investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives.


SteerPlex

Glen Berseth

Levels from Baulder's Gate

SteerPlex works to define a salient set of scenario features that can be used to identify the relative complexity of a steering scenario.