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Niklas Schmid

2 accepted papers

2026

Beyond Domain Randomization: Safety Certificates for Reinforcement Learning

ICRA 2026poster

With the growing acceptance of robotics in daily life there is a growing need for certifiably safe control policies. While simulation provides a safe training environment, policies often fail in sim-to-real transfer. We propose a data-driven certification framework for reinforcement learning based o…

Cited by 0Scholar
2026

Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters

RA-L 2026

Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilistic safety constraints. PRMP

Cited by 0SourceScholar