Elias Goldsztejn

2 accepted papers

2026

Enhancing Classical Motion Planners Using RL with Safety Guarantees

ICRA 2026poster

Classical algorithms for autonomous navigation, while well-understood and safe, require manual parameter tuning by experts to perform well. APPL and similar methods use machine learning to dynamically adjust planner parameters during deployment. This approach maintains the safety of classical system…

Cited by 0 Scholar ↗

2023

PTDRL: Parameter Tuning Using Deep Reinforcement Learning

Elias Goldsztejn, Tal Feiner, Ronen Brafman

IROS 2023poster

A variety of autonomous navigation algorithms exist that allow robots to move around in a safe and fast manner. Many of these algorithms require parameter re-tuning when facing new environments. In this paper, we propose PTDRL, a parameter-tuning strategy that adaptively selects from a fixed set of…

Cited by 2 Source ↗Scholar ↗