DRIP: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
Overview
DRIP, a LiDAR-only planning framework, predicts collision risk via distribution over minimum clearance and uses empirical collision outcomes for training. It outperforms baselines with fewer collisions and less freezing in crowded environments with up to 45 pedestrians in closed-loop tests.
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Reported quantity · pedestrians: 45 other · Basis not reported
“, 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 beh”
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- arXiv Robotics — research abstractsDon't Risk It.: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
The paper presents DRIP, an end-to-end LiDAR-only planning framework that couples occupancy forecasting with trajectory-conditioned clearance and uncertainty estimation. DRIP predicts a distribution over minimum robot-obstacle clearance for candidate control sequences to evaluate collision risk, trained using a risk cost grounded in empirical collision outcomes. In closed-loop experiments with up to 45 pedestrians, DR,IP achieves higher success rates with fewer collisions and reduced freezing behavior compared to state-of-the-art baselines.
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