The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Decision making for self‐driving cars is usually tackled by manually encoding rules from
drivers' behaviours or imitating drivers' manipulation using supervised learning techniques …

In order to intuitively and efficiently collaborate with humans, robots must learn to complete
tasks specified using natural language. We represent natural language instructions as goal …

Gathering the most informative data from humans without overloading them remains an
active research area in AI, and is closely coupled with the problems of determining how and …

The de-facto standard approach in MDP verification is based on value iteration (VI). We
propose compositional VI, a framework for model checking compositional MDPs, that …

Learning composable policies for environments with complex rules and tasks is a
challenging problem. We introduce a hierarchical reinforcement learning framework called …

We propose a novel hierarchical reinforcement learning framework for control with
continuous state and action spaces. In our framework, the user specifies subgoal regions …

The exploration of new environments is a crucial challenge for mobile robots. This task
becomes even more complex with the added requirement of ensuring safety. Here, safety …

Reinforcement learning (RL) has shown promising performance in autonomous driving
applications in recent years. The early end-to-end RL method is usually unexplainable and …

We examine the problem of learning and planning on high-dimensional domains with long
horizons and sparse rewards. Recent approaches have shown great successes in many …