International Journal of Robotics Research
Intelligence without representation
Artificial Intelligence
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Biologically inspired approaches to robotics: what can we learn from insects?
Communications of the ACM
Understanding intelligence
Being There: Putting Brain, Body, and World Together Again
Being There: Putting Brain, Body, and World Together Again
Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Biorobotics
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
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Evolution of functional specialization in a morphologically homogeneous robot
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Accelerating self-modeling in cooperative robot teams
IEEE Transactions on Evolutionary Computation
Active categorical perception in an evolved anthropomorphic robotic arm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving morphology and control: a distributed approach
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Evolving flexible joint morphologies
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Embodied artificial intelligence argues that the body and brain play equally important roles in the generation of adaptive behavior. An increasingly common approach therefore is to evolve an agent's morphology along with its control in the hope that evolution will find a good coupled system. In order for embodied artificial intelligence to gain credibility within the robotics and cognitive science communities, however, it is necessary to amass evidence not only for how to co-optimize morphology and control of adaptive machines, but why. This work provides two new lines of evidence for why this co-optimization is useful: Here we show that for an object manipulation task in which a simulated robot must accomplish one, two, or three objectives simultaneously, subjugating more aspects of the robot's morphology to selective pressure allows for the evolution of better robots as the number of objectives increases. In addition, for robots that successfully evolved to accomplish all of their objectives, those composed of evolved rather than fixed morphologies generalized better to previously unseen environmental conditions.