The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image space, learning a good visual representation requires a considerable amount of visual data. However, when learning in the real world, data is expensive. Sim2Real is a promising paradigm for overcoming data scarcity in the real-world target domain by using a simulator to collect large amounts of cheap data closely related to the target task. However, it is difficult to transfer an image-conditioned policy from sim to real when the domains are very visually dissimilar. To bridge the sim2real visual gap, we propose using natural language descriptions of images as a unifying signal across domains that captures the underlying task-relevant semantics. Our key insight is that if two image observations from different domains are labeled with similar language, the policy should predict similar action distributions for both images. We demonstrate that training the image encoder to predict the language description or the distance between descriptions of a sim or real image serves as a useful, data-efficient pretraining step that helps learn a domain-invariant image representation. We can then use this image encoder as the backbone of an IL policy trained simultaneously on a large amount of simulated and a handful of real demonstrations. Our approach outperforms widely used prior sim2real methods and strong vision-language pretraining baselines like CLIP and R3M by 25 to 40%.

翻译：学习基于图像的机器人策略的主要挑战在于获取有利于底层控制的视觉表征。由于图像空间的高维度性，学习良好的视觉表征需要大量视觉数据。然而，在真实世界中学习时，数据获取成本高昂。Sim2Real（仿真到现实）是一种有前景的范式，它通过使用模拟器收集大量与目标任务密切相关的低成本数据，来克服真实目标域中的数据稀缺问题。但当下游域在视觉上差异显著时，将基于图像的策略从仿真迁移到现实变得困难。为弥合Sim2Real的视觉差距，我们提出利用图像的自然语言描述作为一种跨域统一信号，以捕获与任务相关的底层语义。我们的关键洞察是：若来自不同域的两张图像观测被标记以相似语言，策略应对这两张图像预测相似的动作分布。我们证明，训练图像编码器预测仿真或真实图像的语言描述或描述间距离，是一种高效的数据预训练步骤，有助于学习域不变图像表征。随后，可将此图像编码器作为模仿学习策略的主干网络，该策略同时利用大量仿真数据和少量真实演示进行训练。我们的方法在性能上优于广泛使用的先前Sim2Real方法以及CLIP和R3M等强视觉-语言预训练基线，提升幅度达25%至40%。