论文标题
比较终身学习方法的形态发展的机器人
Comparing lifetime learning methods for morphologically evolving robots
论文作者
论文摘要
不断发展的机器人的形态和控制器同时导致了一个问题：即使父母的身体和大脑良好，随机重组也会破坏这一匹配，并在其后代中导致身体不匹配。我们认为，可以通过让新生机器人执行学习过程来缓解这种方法，该过程在出生后快速优化其遗传的大脑。我们比较了三种不同的算法。为此，我们考虑了三种算法属性，效率，功效以及对运行学习过程的机器人形态差异的敏感性。
Evolving morphologies and controllers of robots simultaneously leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. We argue that this can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. We compare three different algorithms for doing this. To this end, we consider three algorithmic properties, efficiency, efficacy, and the sensitivity to differences in the morphologies of the robots that run the learning process.
