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cs.RO2026
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies
Sajad Marvi, Julian Schmidt, Alessandro Canevaro +2
Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-driven self-play scheme, in…
cs.RO2026
G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance
Hang Yu, Ye Jin, Alessandro Canevaro +7
In autonomous driving, diffusion-based planners have emerged as a promising paradigm for robust motion planning in dense and interactive traffic, as they can effectively model dive…
cs.RO2026
Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning
Alessandro Canevaro, Hang Yu, Julian Schmidt +5
While closed-loop motion planners trained on large-scale, object-level datasets, e.g., nuPlan, demonstrate strong in-distribution (ID) performance, their generalization to novel ur…