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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/z3fknupxejhtji4dnxpbpdltui" style="color: black;">International Journal of Humanoid Robotics</a>
A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its "brain" (or "central nervous system", including the information processor and controller) through online, real-time interactions with its environment (including<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s0219843604000149">doi:10.1142/s0219843604000149</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3bmyboli7jd2lm7ktb2oixaijm">fatcat:3bmyboli7jd2lm7ktb2oixaijm</a> </span>
more »... ). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav developmental robots. The manual and autonomous development paradigms are formulated along with a theory of representation suited for autonomous development. Unlike traditional robot learning, the tasks that a developmental robot ends up learning are unknown during the programming time so that the task-specific representation must be generated and updated through real-time "living" experiences. Experimental results with SAIL and Dav developmental robots are presented, including visual attention selection, autonomous navigation, developmental speech learning, range-based obstacle avoidance, and scaffolding through transfer and chaining.
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