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arXiv · 2610.40050

Don't Risk It!: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation

Mathai Mathew Pulicken·Laksh Nanwani·Basant Sharma·Phani Teja Singamaneni·Arun Kumar Singh·K. Madhava Krishna
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Abstract

Efficient crowd navigation requires anticipating dynamic collision-free space without relying on fragile object tracking. We propose the Don't Risk It Planner (DRIP), an end-to-end LiDAR-only planning framework that couples occupancy forecasting with trajectory-conditioned clearance and uncertainty estimation. Operating directly on LiDAR-derived occupancy grid histories, DRIP predicts a distribution over minimum robot-obstacle clearance for candidate control sequences to evaluate collision risk. The model is trained using a risk cost that grounds predictive uncertainty directly in empirical collision outcomes rather than geometric regression alone. In closed-loop experiments with up to 45 pedestrians, DRIP achieves higher success rates with fewer collisions and reduced freezing behavior compared to state-of-the-art baselines.

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Mathai Mathew Pulicken, Laksh Nanwani, Basant Sharma, Phani Teja Singamaneni, Arun Kumar Singh, K. Madhava Krishna. 2026-10-29. Don't Risk It!: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation. https://arxiv.org/abs/2610.40050

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