论文标题
用肌肉学习：拟人化任务中数据效率和鲁棒性的好处
Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks
论文作者
论文摘要
人类能够在鲁棒性，多功能性和学习各种动作的新任务方面都超越机器人。我们假设高度非线性的肌肉动力学在提供固有的稳定性方面起着重要作用，这有利于学习。虽然在模拟和机器人技术中将现代学习技术应用于肌肉动态系统的最新进展，但到目前为止，尚未进行详细的分析以显示从Scratch学习时肌肉的好处。我们的研究缩小了这一差距，并在数据效率，超参数敏感性和鲁棒性方面展示了肌肉执行器对核心机器人技术挑战的潜力。
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 when learning from scratch. Our study closes this gap and showcases the potential of muscle actuators for core robotics challenges in terms of data-efficiency, hyperparameter sensitivity, and robustness.
