  Traditional Evolutionary Robotics (ER) employs evolutionary techniques to
search for a single monolithic controller which can aid a robot to learn a
desired task. These techniques suffer from bootstrap and deception issues when
the tasks are complex for a single controller to learn. Behaviour-decomposition
techniques have been used to divide a task into multiple subtasks and evolve
separate subcontrollers for each subtask. However, these subcontrollers and the
associated subcontroller arbitrator(s) are all evolved off-line. A distributed,
fully embodied and evolutionary version of such approaches will greatly aid
online learning and help reduce the reality gap. In this paper, we propose an
immunology-inspired embodied action-evolution cum selection algorithm that can
cater to distributed ER. This algorithm evolves different subcontrollers for
different portions of the search space in a distributed manner just as
antibodies are evolved and primed for different antigens in the antigenic
space. Experimentation on a collective of real robots embodied with the
algorithm showed that a repertoire of antibody-like subcontrollers was created,
evolved and shared on-the-fly to cope up with different environmental
conditions. In addition, instead of the conventionally used approach of
broadcasting for sharing, we present an Intelligent Packet Migration scheme
that reduces energy consumption.
