激光雷达导航中更强大的自编码器的陷阱
The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation
摘要：
与对原始观察值进行训练相比，预训练的自动编码器用于强化学习的好处已经众所周知[1]。在本文中，我们解决了一个紧凑的和信息丰富的状态表示的生成。特别是，我们训练一个用于二维激光雷达扫描的变分自编码器，以使用其潜在状态进行导航任务的强化学习。为了实现我们的自动编码流水线的高重建功率，我们提出了一个-在自动编码2D激光雷达扫描的上下文中-新的预处理成本地二进制占用图像。这没有额外的要求，既没有自定位也没有鲁棒的映射，因此可以应用于任何设置，并且可以很容易地从现实世界的模拟中转移。在第二阶段中，我们展示了在简单的导航任务中使用我们的自动编码管道生成的紧凑状态表示，并暴露了增加重建功率总是会导致性能提高的陷阱。我们使用tensorflow在python中实现了我们的方法。我们的数据集使用pybullet进行模拟，并使用slamtec rplidar A3进行记录。实验表明，我们的方法的2D激光雷达扫描w.r.t.的重建能力显着提高。然而，正如我们在实验中所证明的，当改进由自动编码流水线生成的潜在状态表示时，对基于激光雷达的导航任务中的强化学习的影响是不可预测的。这是令人惊讶的，需要在为强化学习任务优化预训练的自动编码器的过程中加以考虑。
英文摘要：
The benefit of pretrained autoencoders for reinforcement learning in comparison to training on raw observations is already known [1]. In this paper, we address the generation of a compact and information-rich state representation. In particular, we train a variational autoencoder for 2D-lidar scans to use its latent state for reinforcement learning of navigation tasks. To achieve high reconstruction power of our autoencoding pipeline, we propose an - in the context of autoencoding 2D-lidar scans - novel preprocessing into a local binary occupancy image. This has no additional requirements, neither self-localization nor robust mapping, and therefore can be applied in any setting and easily transferred from simulation in real-world. In a second stage, we show the usage of the compact state representation generated by our autoencoding pipeline in a simplistic navigation task and expose the pitfall that increased reconstruction power will always lead to an improved performance. We implemented our approach in python using tensorflow. Our datasets are simulated with pybullet as well as recorded using a slamtec rplidar A3. The experiments show the significantly improved reconstruction capabilities of our approach for 2D-lidar scans w.r.t. the state of the art. However, as we demonstrate in the experiments the impact on reinforcement learning in lidar-based navigation tasks is non-predictable when improving the latent state representation generated by an autoencoding pipeline. This is surprising and needs to be taken into account during the process of optimizing a pretrained autoencoder for reinforcement learning tasks.
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