The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Recently, deep reinforcement learning (DRL) methods have achieved impressive
performance on tasks in a variety of domains. However, neural network policies produced …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

In this paper, we consider the problem of learning safe policies for probabilistic-constrained
reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …

One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous
methods employ the empirical risk minimization principle in optimization. However, the …

When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …

We consider the problem of reinforcement learning when provided with (1) a baseline
control policy and (2) a set of constraints that the learner must satisfy. The baseline policy …