Many robotics problems, from robot motion planning to object manipulation, can be modeled as mixed-integer convex programs (MICPs). However, state-of-the-art algorithms are still unable solve MICPs for control problems quickly enough online use and existing heuristics typically only find suboptimal solutions that might degrade performance. In this work, we turn data-driven methods present the C...

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with perturbation vector features that alters prediction result. Given vector, can interpret it an "action" obtaining one's desired decision In practice, however, showing only of...

Endogenous, i.e. decision-dependent, uncertainty has received increased interest in the stochastic programming community. In robust optimization context, however, it rarely been considered. This work addresses multistage mixed-integer with decision-dependent sets. The proposed framework allows us to consider both continuous and integer recourse, including recourse decisions that affect set. We ...