Learning Multi-Agent Navigation from Human Crowd Data

The task of safely steering agents amidst static and dynamic obstacles has many applications in robotics, graphics, and traffic engineering. While decentralized solutions are essential for scalability and robustness, achieving globally efficient motions for the entire system of agents is equally important. In a traditional decentralized setting, each agent relies on an underlying local planning algorithm that takes as input a preferred velocity and the current state of the agent\u27s neighborhood and then computes a new velocity for the next time-step that is collision-free and as close as possible to the preferred one. Typically, each agent promotes a goal-oriented preferred velocity, which can result in myopic behaviors as actions that are locally optimal for one agent is not necessarily optimal for the global system of agents. In this thesis, we explore a human-inspired approach for efficient multi-agent navigation that allows each agent to intelligently adapt its preferred velocity based on feedback from the environment. Using supervised learning, we investigate different egocentric representations of the local conditions that the agents face and train various deep neural network architectures on extensive collections of human trajectory datasets to learn corresponding life-like velocities. During simulation, we use the learned velocities as high-level, preferred velocities signals passed as input to the underlying local planning algorithm of the agents. We evaluate our proposed framework using two state-of-the-art local methods, the ORCA method, and the PowerLaw method. Qualitative and quantitative results on a range of scenarios show that adapting the preferred velocity results in more time- and energy-efficient navigation policies, allowing agents to reach their destinations faster as compared to agents simulated with vanilla ORCA and PowerLaw

Synchronizing navigation algorithms for crowd simulation via topological strategies

International audienceWe present a novel topology-driven method for enhancing the navigation behavior of agents in virtual environments and crowds. In agent-based crowd simulations, each agent combines multiple navigation algorithms for path planning, collision avoidance, and more. This may lead to undesired motion whenever the algorithms disagree on how an agent should pass an obstacle or another agent. In this paper, we argue that all navigation algorithms yield a strategy: a set of decisions to pass obstacles and agents along the left or right. We show how to extract such a strategy from a (global) path and from a (local) velocity. Next, we propose a general way for an agent to resolve conflicts between the strategies of its algorithms. For example, an agent may re-plan its global path when collision avoidance suggests a detour. As such, we bridge conceptual gaps between algorithms, and we synchronize their results in a fundamentally new way. Experiments with an example implementation show that our strategy concept can improve the behavior of agents while preserving real-time performance. It can be applied to many agent-based simulations, regardless of their specific navigation algorithms. The concept is also suitable for explicitly sending agents in particular directions, e.g. to simulate signage