Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …

Deep reinforcement learning has recently made significant progress in solving computer
games and robotic control tasks. A known problem, though, is that policies overfit to the …

This article attempts to place the emergence of probabilistic numerics as a mathematical–
statistical research field within its historical context and to explore how its gradual …

We formulate probabilistic numerical approximations to solutions of ordinary differential
equations (ODEs) as problems in Gaussian process (GP) regression with nonlinear …

ML-based motion planning is a promising approach to produce agents that exhibit complex
behaviors, and automatically adapt to novel environments. In the context of autonomous …

Accumulating evidence from human-based research has highlighted that the prevalent one-
size-fits-all approach for neural and behavioral interventions is inefficient. This approach can …

This paper presents new quadrature rules for functions in a reproducing kernel Hilbert space
using nodes drawn by a sampling algorithm known as randomly pivoted Cholesky. The …

Reinforcement learning (RL) applications require a huge effort to become established in real-
world environments, due to the injury and break down risks during interactions between the …

Morphologically evolving robot systems need to include a learning period right after 'birth'to
acquire a controller that fits the newly created body. In this paper, we investigate learning …