This paper presents an active learning strategy for robotic systems that takes into account
task information, enables fast learning, and allows control to be readily synthesized by …

Much current work in robotics focuses on the development of robots capable of autonomous
unsupervised learning. An essential prerequisite for such learning to be possible is that the …

This article develops KL-ergodic exploration from equilibrium (KL-E 3), a method for robotic
systems to integrate stability into actively generating informative measurements through …

Infants learn to speak rapidly during their first years of life, gradually improving from simple
vowel-like sounds to larger consonant-vowel complexes. Learning to control their vocal tract …

Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial
agents that can act in complex environments. In real-world scenarios, tasks may be …

The redundancy inherent to the human body is a central problem that must be solved by the
brain when acquiring new motor skills. The problem of redundancy becomes particularly …

Intrinsic motivations have been successfully employed in machine learning and robotics to
improve the autonomous acquisition of knowledge and skills. While forming an ample …

In open-ended continuous environments, robots need to learn multiple parameterised
control tasks in hierarchical reinforcement learning. We hypothesise that the most complex …

We use goal babbling to bootstrap a parametric model of speech production for a complex
3D vocal tract model. The system learns to control the articulators for producing five different …

Animal learning is driven not only by biological needs but also by intrinsic motivations (IMs)
serving the acquisition of knowledge. Computational modeling involving IMs is indicating …