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Interactive Trajectory Planning with Learning-based Distr...
Erik B\"orve · 2026-05-11 · via cs.LG updates on arXiv.org

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Abstract:We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to first learn the decision distribution and solve a Stochastic Model Predictive Control (SMPC) problem. To account for errors in the learned distribution, we show that it is possible to utilize Probably Approximately Correct (PAC) learning in combination with Distributionally Robust (DR) optimization to obtain a solution which accounts for the errors induced by the learning model. The results indicate that our PAC learning-based DR-MPC framework provides a method to interpolate between a robust MPC and an omnipotent SMPC, based on the available number of samples.
Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG)
Cite as: arXiv:2605.07768 [eess.SY]
  (or arXiv:2605.07768v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2605.07768

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Erik Börve [view email]
[v1] Fri, 8 May 2026 14:09:54 UTC (75 KB)