Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving visually guided robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Communication in reactive multiagent robotic systems
Autonomous Robots
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Biorobotics
Hardware Solutions for Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Multi-robot learning with particle swarm optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Action-selection and crossover strategies for self-modeling machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Exploiting multiple robots to accelerate self-modeling
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
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One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.