论文标题
dirl：用于SIM到现实转移的域不变表示学习
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer
论文作者
论文摘要
在模拟中生成大规模的合成数据是为基于培训视觉的深度学习模型收集/标记真实数据的可行替代方法，尽管建模不准确并不能推广到物理世界。在本文中，我们提出了一种域不变的表示学习（DIRL）算法，以使用少量的真实数据使深层模型适应物理环境。现有的方法仅通过对齐整个域的边际分布并假设条件分布是域不变的方法，从而减轻协变量转移，这在实际情况下会导致模棱两可的转移。我们建议将边缘（输入域）和条件（输出标签）分布结合起来，以通过对抗性学习来减轻协变量和条件偏移，并将其与三胞胎分布损失相结合，以使共享特征空间中的条件分布不相同。关于数字域的实验在具有挑战性的基准上产生了最先进的性能，而使用移动机器人使用移动机器人进行基于视觉的对象识别的对象识别的转移将从26.8％提高到91.0％，从而使各种物体的精度达到86.5％。代码和补充详细信息可从https://sites.google.com/view/dirl获得
Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper, we present a domain-invariant representation learning (DIRL) algorithm to adapt deep models to the physical environment with a small amount of real data. Existing approaches that only mitigate the covariate shift by aligning the marginal distributions across the domains and assume the conditional distributions to be domain-invariant can lead to ambiguous transfer in real scenarios. We propose to jointly align the marginal (input domains) and the conditional (output labels) distributions to mitigate the covariate and the conditional shift across the domains with adversarial learning, and combine it with a triplet distribution loss to make the conditional distributions disjoint in the shared feature space. Experiments on digit domains yield state-of-the-art performance on challenging benchmarks, while sim-to-real transfer of object recognition for vision-based decluttering with a mobile robot improves from 26.8 % to 91.0 %, resulting in 86.5 % grasping accuracy of a wide variety of objects. Code and supplementary details are available at https://sites.google.com/view/dirl
