Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Emergence of functional modularity in robots
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
Embodied cognition: a field guide
Artificial Intelligence
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
How robot morphology and training order affect the learning of multiple behaviors
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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A central tenet of embodied artificial intelligence is that intelligent behavior arises out of the coupled dynamics between an agent's body, brain and environment. It follows that the complexity of an agents's controller and morphology must match the complexity of a given task. However, more complex task environments require the agent to exhibit different behaviors, which raises the question as to how to distribute responsibility for these behaviors across the agents's controller and morphology. In this work a robot is trained to locomote and manipulate an object, but the assumption of functional specialization is relaxed: the robot has a segmented body plan in which the front segment may participate in locomotion and object manipulation, or it may specialize to only participate in object manipulation. In this way, selection pressure dictates the presence and degree of functional specialization rather than such specialization being enforced a priori. It is shown that for the given task, evolution tends to produce functionally specialized controllers, even though successful generalized controllers can also be evolved. Moreover, the robot's initial conditions and training order have little effect on the frequency of finding specialized controllers, while the inclusion of additional proprioceptive feedback increases this frequency.