2 papers
cs.LG2026
Stochastic Decision Horizons for Constrained Reinforcement Learning
Nikola Milosevic, Leonard Franz, Daniel Haeufle +3
We propose stochastic decision horizons (SDH), a theoretically grounded framework for solving constrained RL problems with every-step constraint satisfaction, a desirable property…
cs.RO2026
Confidence-Gated Robot Autonomy: When Does Uncertainty Actually Help?
Johannes A. Gaus, Jhon P. F. Charaja, Daniel Haeufle
Robotic systems often use predictive uncertainty to decide whether to act autonomously or defer to a fallback policy. In threshold-gated autonomy, uncertainty matters mainly throug…