3 papers
cs.RO2026
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequenc…
cs.AI2026
Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration
Yuval Aroosh, Ayal Taitler
Differentiable planning enables gradient-based optimization of decision-making problems by leveraging differentiable models of system dynamics. However, in highly nonlinear and hyb…
math.OC2025
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs
Michael Gimelfarb, Ayal Taitler, Scott Sanner
We propose the Constraint-Generation Policy Optimization (CGPO) framework to optimize policy parameters within compact and interpretable policy classes for mixed discrete-continuou…