We present a three-level cognitive system in a learning by demonstration context. The
system allows for learning and transfer on the sensorimotor level as well as the planning …

Abstract Model-based reinforcement learning is a powerful paradigm for learning tasks in
robotics. However, in-depth exploration is usually required and the actions have to be …

Learning from demonstration provides an attractive solution to the problem of teaching
autonomous systems how to perform complex tasks. Reward learning from demonstration is …

The soundness and optimality of a plan depends on the correctness of the domain model.
Specifying complete domain models can be difficult when interactions between an agent …

Research in learning from demonstration can generally be grouped into either imitation
learning or intention learning. In imitation learning, the goal is to imitate the observed …

Acting in domains where an agent must plan several steps ahead to achieve a goal can be a
challenging task, especially if the agentʼs sensors provide only noisy or partial information …

Probabilistic planners have improved recently to the point that they can solve difficult tasks
with complex and expressive models. In contrast, learners cannot tackle yet the expressive …

Our goal is to develop methods for non-experts to teach complex behaviors to autonomous
agents (such as robots) by accommodating “natural” forms of human teaching. We built a …

Efficient exploration is widely recognized as a fundamental challenge inherent in
reinforcement learning. Algorithms that explore efficiently converge faster to near-optimal …

We describe a system allowing a robot to learn goal-directed manipulation sequences such
as steps of an assembly task. Learning is based on a free mix of exploration and instruction …