Decision trees are a popular choice of explainable model, but just like neural networks, they suffer from adversarial examples. Existing algorithms for fitting decision robust against examples greedy heuristics and lack approximation guarantees. In this paper we propose ROCT, collection methods to train that optimally user-specified attack models. We show the min-max optimization problem arises...

Robot hybrid control has been noted for its difficulty due to the so-called dynamic stability problem, and consequently demands robust control algorithms that can overcome this difficulty, yet with accuracy. In this paper, • control law based on time-delay controlhas been derived for hybrid control with • condition for closed-loop stability. In addition, its relationship with the disturbance ob...

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us t...