论文标题
与模型的机器人学习
On-Robot Learning With Equivariant Models
论文作者
论文摘要
最近，现象已证明了模型的神经网络模型可以提高计算机视觉和增强学习中任务的样本效率。本文在机器人策略学习的背景下探讨了这一想法，其中必须完全在物理机器人系统上学习策略，而无需参考模型，模拟器或脱机数据集。我们将重点放在模棱两可的SAC在机器人操作中的应用，并探索算法的多种变化。最终，我们证明了通过在不到一个小时或两个小时的壁时钟时间内的机上体验完全学习几个非平凡操纵任务的能力。
Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in the context of on-robot policy learning in which a policy must be learned entirely on a physical robotic system without reference to a model, a simulator, or an offline dataset. We focus on applications of Equivariant SAC to robotic manipulation and explore a number of variations of the algorithm. Ultimately, we demonstrate the ability to learn several non-trivial manipulation tasks completely through on-robot experiences in less than an hour or two of wall clock time.
