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.

Petros Faloutsos Articles


Deep Integration of Physical Humanoid Control and Crowd Navigation

Brandon Haworth, Glen Berseth, Seonghyeon Moon, Petros Faloutsos, Mubbasir Kapadia

Many multi-agent navigation approaches make use of simplified representations such as a disk. These simplifications allow for fast simulation of thousands of agents but limit the simulation accuracy and fidelity. In this paper, we propose a fully integrated physical character control and multi-agent navigation method. In place of sample complex online planning methods, we extend the use of recent deep reinforcement learning techniques. This extension improves on multi-agent navigation models and simulated humanoids by combining Multi-Agent and Hierarchical Reinforcement Learning. We train a single short term goal-conditioned low-level policy to provide directed walking behaviour. This task-agnostic controller can be shared by higher-level policies that perform longer-term planning. The proposed approach produces reciprocal collision avoidance, robust navigation, and emergent crowd behaviours. Furthermore, it offers several key affordances not previously possible in multi-agent navigation including tunable character morphology and physically accurate interactions with agents and the environment. Our results show that the proposed method outperforms prior methods across environments and tasks, as well as, performing well in terms of zero-shot generalization over different numbers of agents and computation time.


Interactive Architectural Design with Diverse Solution Exploration

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

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.


Gamification of Crowd-Driven Environment Design

Michael Brandon Haworth, Muhammad Usman, Davide Schaumann, Nilay Chakraborty, Glen Berseth, Petros Faloutsos, Mubbasir Kapadia

This paper describes using human creativity within a gamified collaborative design framework to address the complexity of predictive environment design. This framework is predicated on gamifying crowd objectives and presenting environment design problems as puzzles. A usability study reveals that the framework is considered usable for the task. Participants were asked to configure an environment puzzle to reduce an important crowd metric, the total egress time. The design task was constructed to be straightforward and uses a simplified environment as a probe for understanding the utility of gamification and the performance of collaboration. Single-player and multiplayer designs outperformed both optimization and expert-sourced designs of the same environment and multiplayer designs further outperformed the single-player designs. Single-player and multiplayer iterations followed linear and exponential decrease trends in total egress time respectively. Our experiments provide strong evidence towards an interesting novel approach of crowdsourcing collaborative environment design.


Evaluating and Optimizing Evacuation Plans for Crowd Egress

Vincius J Cassol, Estêvão Smania Testa, Cláudio Rosito Jung, Muhammad Usman, Petros Faloutsos, Glen Berseth, Mubbasir Kapadia, Norman I Badler, Soraia Raupp Musse

Evacuation planning is an important and difficult task in building design. The proposed framework can identify optimal evacuation plans using decision points, which control the ratio of agents that select a particular route at a specific spatial location. The authors optimize these ratios to achieve the best evacuation based on a quantitatively validated metric for evacuation performance. This metric captures many of the important aspects of an evacuation: total evacuation time, average evacuation time, agent speed, and local agent density. The proposed approach was validated using a night club model that incorporates real data from an actual evacuation.


Towards Computer Assisted Crowd Aware Architectural Design

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

We present a preliminary exploration of an architectural optimization process towards a computational tool for designing environments (e.g., building floor plans). Using dynamic crowd simulators we derive the fitness of architectural layouts. The results of the simulation are used to provide feedback to a user in terms of crowd animation, aggregate statistics, and heat maps. Our approach automatically optimizes the placement of environment elements to maximize the flow of the crowd, while satisfying constraints that are imposed by the user (e.g., immovable walls or support bearing structures). 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 perform a small scale user study to obtain early feedback on the performance and quality of our method in contrast with a manual approach.


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.


ACCLMesh: Curvature-Based Navigation Mesh Generation

Glen Berseth, Mubbasir Kapadia, Petros Faloutsos

The proposed method robustly and efficiently computes a navigation mesh for arbitrary and dynamic 3D environments based on curvature. This method addresses a number of known limitations in state-of-the-art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions and can robustly handle complex surfaces. The method is integrated into a standard navigation and collision-avoidance system to simulate thousands of agents on complex 3D surfaces in real-time.


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.


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.