A Robotic Platform for Scalable Life-Long Learning Experiments

A grand goal of developmental robotics is to build robots capable to learn continuously novel sensorimotor and social skills over extended periods of time, i.e. months and years. This implies a huge methodological challenge: the techniques elaborated for this aim should be evaluated on real life-long experiments. Yet, so far the vast majority of robot learning experiments have been limited to a few hours, if not a few minutes. One of the reasons is that no experimental robotic platforms allowed for such long experiments. Ideally, such a platform should be robust and reconfigurable. But because it should allow for little constrained exploration and physical interaction with humans, it should be both safe (one can expect the learning robot to try wild movement when interacting with humans), cheap and easy to repair (breaking is unavoidable). Industrial robots are robust, but they are not often reconfigurable and too dangerous to allow unconstrained exploration. Recent industrial quality soft compliant robots are still too brittle and expensive for such experiments. On the contrary, most low cost platforms are not robust enough, and do not provide the required compliance properties. In this talk, I will present a novel experimental platform which breaks this experimental barrier. It integrates and uses in an original manner various off-the-shelf hardware and software components, allowing for robust, precise, cheap, compliant easily repairable robots. I will illustrate two instantiations of this platform: 1) the Acroban humanoid robot, allowing for whole-body intuitive physical interaction with human children (http://flowers.inria.fr/acroban.php); 2) The Ergo-Robot experiment, which has been running curiosity-driven learning and human-robot interaction algorithms continuously for 5 months in a public exhibition space (http://flowers.inria.fr/ergo-robots.php).