标题： 基于深度强化学习的机器人操作中的创造力 显示英文标题

标题： Creativity in Robot Manipulation with Deep Reinforcement Learning

摘要： 深度强化学习（DRL）已成为机器人科学中一种强大的控制技术。与控制理论相比，DRL在全面探索环境方面更加稳健。当应用于机器人时，DRL的这一能力生成了更接近人类的行为和智能。为了探索这种能力，我们设计了具有挑战性的操作任务，以观察机器人处理复杂场景的策略。我们发现，机器人不仅能够成功完成任务，还会展现出创造性和非直观的解决方案。我们还观察到机器人在接近成功时的坚持，以及其在决定继续还是放弃时的敏锐能力。 显示英文摘要

摘要： Deep Reinforcement Learning (DRL) has emerged as a powerful control technique in robotic science. In contrast to control theory, DRL is more robust in the thorough exploration of the environment. This capability of DRL generates more human-like behaviour and intelligence when applied to the robots. To explore this capability, we designed challenging manipulation tasks to observe robots strategy to handle complex scenarios. We observed that robots not only perform tasks successfully, but also transpire a creative and non intuitive solution. We also observed robot's persistence in tasks that are close to success and its striking ability in discerning to continue or give up.