  Autonomous discovery and direct instruction are two distinct sources of
learning in children but education sciences demonstrate that mixed approaches
such as assisted discovery or guided play result in improved skill acquisition.
In the field of Artificial Intelligence, these extremes respectively map to
autonomous agents learning from their own signals and interactive learning
agents fully taught by their teachers. In between should stand teachable
autotelic agents (TAA): agents that learn from both internal and teaching
signals to benefit from the higher efficiency of assisted discovery. Designing
such agents will enable real-world non-expert users to orient the learning
trajectories of agents towards their expectations. More fundamentally, this may
also be a key step to build agents with human-level intelligence. This paper
presents a roadmap towards the design of teachable autonomous agents. Building
on developmental psychology and education sciences, we start by identifying key
features enabling assisted discovery processes in child-tutor interactions.
This leads to the production of a checklist of features that future TAA will
need to demonstrate. The checklist allows us to precisely pinpoint the various
limitations of current reinforcement learning agents and to identify the
promising first steps towards TAA. It also shows the way forward by
highlighting key research directions towards the design or autonomous agents
that can be taught by ordinary people via natural pedagogy.
