Don't Risk It.: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
Don'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.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
pedestrians
≤45
View original evidence
, 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
Open source S3
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
, 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
Open source S3
arXiv:2610.40050v1 Announce Type: new 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
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org