Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles in this setting. Our study closes this gap by investigating core robotics challenges and comparing the performance of different actuator morphologies in terms of data-efficiency, hyperparameter sensitivity, and robustness.
翻译:人类在强健、多功能和在各种运动中学习新任务方面能够超越机器人的性能。我们假设高度非线性肌肉动态在提供固有的稳定性方面起着重要作用,这有利于学习。虽然在将现代学习技术应用于模拟和机器人的肌肉活化系统方面最近有所进步,但迄今还没有进行详细分析,以显示肌肉在这个环境中的好处。我们的研究通过调查核心机器人的挑战和比较数据效率、超光谱灵敏度和坚固度方面不同动体形态的性能,缩小了这一差距。