Controllable Crowd Generation through World-Model Planning
To address this limitation, we propose Ctrl-CWM, a multi-agent Controllable Crowd World Model that integrates crowd generation and run-time control.
ProofPaper ↗
Key points
- Crowd simulation plays a central role in robot navigation, autonomous driving, and urban planning.
- To this end, Ctrl-CWM consists of an encoder that learns a representation of human motion dynamics, an actor that proposes pedestrian displacements, a critic that evaluates imagined crowd trajectories, and a planner that selects actions.
- Repeated planning advances the simulated crowd, while additional user costs introduce new control objectives without retraining.
- Ctrl-CWM outperforms the state-of-the-art method on most crowd realism and collision metrics, and adapts crowd behaviors to user-specified objectives introduced during simulation.
Sources (1)
- [1]Controllable Crowd Generation through World-Model PlanningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:50 AM
To address this limitation, we propose Ctrl-CWM, a multi-agent Controllable Crowd World Model that integrates crowd generation and run-time control.
Crowd simulation plays a central role in robot navigation, autonomous driving, and urban planning.
Extractive summary: sentences quoted from the sources.
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