Time-aware Motion Planning in Dynamic Environments with Conformal Prediction

Kaier Liang, Licheng Luo, Yixuan Wang, Mingyu Cai, Cristian Ioan Vasile
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:183-195, 2026.

Abstract

Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-liang26a, title = {Time-aware Motion Planning in Dynamic Environments with Conformal Prediction}, author = {Liang, Kaier and Luo, Licheng and Wang, Yixuan and Cai, Mingyu and Vasile, Cristian Ioan}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {183--195}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/liang26a/liang26a.pdf}, url = {https://proceedings.mlr.press/v331/liang26a.html}, abstract = {Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments.} }
Endnote
%0 Conference Paper %T Time-aware Motion Planning in Dynamic Environments with Conformal Prediction %A Kaier Liang %A Licheng Luo %A Yixuan Wang %A Mingyu Cai %A Cristian Ioan Vasile %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-liang26a %I PMLR %P 183--195 %U https://proceedings.mlr.press/v331/liang26a.html %V 331 %X Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments.
APA
Liang, K., Luo, L., Wang, Y., Cai, M. & Vasile, C.I.. (2026). Time-aware Motion Planning in Dynamic Environments with Conformal Prediction. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:183-195 Available from https://proceedings.mlr.press/v331/liang26a.html.

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