arXiv Computer Vision
Reinforcement Learning for Adaptive Crowd Flow Control Using Dynamic Barriers
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 8
Controllable Crowd Generation through World-Model Planning
The paper introduces Ctrl-CWM, a multi‑agent controllable crowd world model that merges crowd generation with run‑time control. It uses an encoder to learn human motion dynamics, an actor to propose pedestrian displacements, a critic to evaluate imagined trajectories, and a planner that selects actions based on critic scores and user‑defined costs. Experiments show that Ctrl‑CWM outperforms state‑of‑the‑art methods in realism and collision metrics while allowing new control objectives to be added without retraining.
arXiv Machine Learning
Sep 30
Learning Social Navigation from Internet Videos in the Policy State Space
arXiv:2609.37476v1 Announce Type: cross Abstract: Training robust social-navigation policies requires simulators with diverse scene layouts, terrain, and human motion, but constructing such environme...
arXiv Machine Learning
Aug 28
Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
The paper introduces Planning Diffusion Policy Optimization (PDPO), an offline‑to‑online reinforcement‑learning framework that employs a diffusion policy to produce short‑horizon action chunks for robot crowd navigation. PDPO is pretrained on collision‑avoidance demonstrations and fine‑tuned online with PPO, generating five‑step action sequences applied in a receding‑horizon manner. The authors also identify a benchmark artifact where agents can leave the valid domain without explicit boundary constraints, and they mitigate this by treating boundary violations as collisions, leading to improved success rates over strong baselines.
arXiv AI
Aug 12
Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds
arXiv:2608. 10056v1 Announce Type: cross Abstract: Following a target human in crowded environments involves an inherent conflict between staying close to the target and navigating safely among surrounding pedestrians and obstacles.
arXiv AI
Aug 12
Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning
arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.
arXiv Computer Vision
Oct 8
Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
The paper introduces a cluster-based method for predicting dense crowd trajectories, grouping individuals with similar attributes over time to accelerate processing. By replacing individual pedestrian inputs with cluster centroids, the approach reduces computational cost and memory usage while preserving accuracy. Extensive evaluations on challenging dense crowd scenes confirm these benefits compared to state‑of‑the‑art methods.