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abstract: '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.'
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