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.

Mubbasir Kapadia 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.


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.


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.