Editorial: Intrinsically Motivated Open-Ended Learning in Autonomous Robots.

Notwithstanding the important advances in Artificial Intelligence (AI) and robotics, artificial agents still lack the necessary autonomy and versatility to properly interact with realistic environments. This requires agents to face situations that are unknown at design time, to autonomously discover multiple goals/tasks, and to be endowed with learning processes able to solve multiple tasks incrementally and online.Starting in developmental robotics (Lungarella et al., 2003; Cangelosi and Schlesinger, 2015), and gradually expanding into other fields, intrinsically motivated learning (sometimes called “curiosity-driven learning”) has been studied by many researchers as an approach to autonomous lifelong learning in machines (Oudeyer et al., 2007; Schmidhuber, 2010; Barto, 2013; Mirolli and Baldassarre, 2013). Inspired by the ability of humans and other mammals to discover how to produce “interesting” effects in the environment driven by self-generated motivational signals not related to specific tasks or instructions (White, 1959; Berlyne, 1960; Deci and Ryan, 1985), the research in the field of intrinsically motivated open-ended learning aims to develop agents that autonomously generate motivational signals (Merrick, 2010) to acquire repertoires of diverse skills that are likely to become useful later when specific “extrinsic” tasks need to be performed (eg, Barto et al., 2004; Baldassarre, 2011; Baranes and Oudeyer, 2013; Kulkarni et al., 2016; Santucci et al., 2016).