Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware. Ideally, the system would adapt to the real world by training directly on physical systems. However, this is generally difficult due to the large amount of training data required by most grasp learning models. In this paper, we note that the planar grasp function is $\SE(2)$-equivariant and demonstrate that this structure can be used to constrain the neural network used during learning. This creates an inductive bias that can significantly improve the sample efficiency of grasp learning and enable end-to-end training from scratch on a physical robot with as few as $600$ grasp attempts. We call this method Symmetric Grasp learning (SymGrasp) and show that it can learn to grasp ``from scratch'' in less that 1.5 hours of physical robot time.

翻译：现实世界中，由于抓取动力学的随机性与硬件噪声，抓取检测极具挑战性。理想情况下，系统应通过直接在物理系统上训练来适应真实环境。然而，大多数抓取学习模型所需的大量训练数据通常使这一过程变得困难。本文指出，平面抓取函数具有$\SE(2)$等变性，并证明该结构可用于约束学习过程中使用的神经网络。这一归纳偏置能够显著提升抓取学习的样本效率，使得仅需$600$次抓取尝试即可在物理机器人上实现从头开始的端到端训练。我们将该方法称为对称抓取学习（SymGrasp），并证明它能在不到1.5小时的物理机器人运行时间内学会"从零开始"抓取。