复杂地形更易显现机器人终身学习的优势
More complex environments may be required to discover benefits of lifetime learning in evolving robots
- 对比平坦与山地环境，测试终身学习效果
- 山地环境中学习带来的性能提升更显著
- 适合研究进化机器人与自适应学习的学者
已知在生命周期内进行额外的控制器优化（即内生命周期学习）对演化机器人形态的运动能力有帮助。本文通过对比两种不同环境——平坦环境和更具挑战性的山地环境——进一步探究这一现象。结果表明，在山地环境中，学习带来的收益显著高于平坦环境；因此，若要充分展现学习的优势，可能需要在更复杂的环境中评估机器人性能。
原文摘要 · Abstract (English)
It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we investigate this further by comparing it in two different environments: an easy flat environment and a more challenging hills environment. We show that learning is significantly more beneficial in a hilly environment than in a flat environment and that it might be needed to evaluate robots in a more challenging environment to see the benefits of learning.
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