论文标题
通过深厚的增强学习来改善辅助机器人技术
Improving Assistive Robotics with Deep Reinforcement Learning
论文作者
论文摘要
辅助机器人技术是一类机器人技术，涉及帮助人类在日常护理任务中，由于残疾或年龄，它们可能被抑制。尽管研究表明，经典控制方法可用于设计政策以完成这些任务，但这些方法可能很难推广到任务的各种实例。强化学习可以为此问题提供解决方案，在该问题中，在模拟中训练了机器人，并将其政策转移到现实世界中。在这项工作中，我们复制了公开的基准，用于培训机器人在辅助健身环境中的三个任务上，并探讨了复发性神经网络和阶段性政策梯度学习的用法，以增强原始工作。我们的基线实施符合或超过原始工作的基线，但是，我们发现我们对新方法的探索并不像我们预期的那样有效。我们讨论了我们的基线结果，以及为什么我们的新方法不那么成功的一些想法。
Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize to a variety of instantiations of a task. Reinforcement learning can provide a solution to this issue, wherein robots are trained in simulation and their policies are transferred to real-world machines. In this work, we replicate a published baseline for training robots on three tasks in the Assistive Gym environment, and we explore the usage of a Recurrent Neural Network and Phasic Policy Gradient learning to augment the original work. Our baseline implementation meets or exceeds the baseline of the original work, however, we found that our explorations into the new methods was not as effective as we anticipated. We discuss the results of our baseline and some thoughts on why our new methods were not as successful.
