We present our approach for robotic perception in cluttered scenes that led
to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small
objects with shiny and transparent surfaces, the biggest challenge of the 2017
competition was the introduction of unseen categories. In contrast to
traditional approaches which require large collections of annotated data and
many hours of training, the task here was to obtain a robust perception
pipeline with only few minutes of data acquisition and training time. To that
end, we present two strategies that we explored. One is a deep metric learning
approach that works in three separate steps: semantic-agnostic boundary
detection, patch classification and pixel-wise voting. The other is a
fully-supervised semantic segmentation approach with efficient dataset
collection. We conduct an extensive analysis of the two methods on our ARC 2017
dataset. Interestingly, only few examples of each class are sufficient to
fine-tune even very deep convolutional neural networks for this specific task.

介绍了在混乱场景中进行机器人感知的方法，在 Amazon 机器人挑战赛中获得胜利。作者提出了两种策略：一种是基于深度度量学习的方法，另一种是基于全监督语义分割方法的方法，并在数据集上进行了全面分析。两种方法都有良好效果，值得进一步研究。

有限训练数据的语义分割

Semantic Segmentation from Limited Training Data

The Amazon Robotics Challenge enlisted sixteen teams to each design a
pick-and-place robot for autonomous warehousing, addressing development in
robotic vision and manipulation. This paper presents the design of our
custom-built, cost-effective, Cartesian robot system Cartman, which won first
place in the competition finals by stowing 14 (out of 16) and picking all 9
items in 27 minutes, scoring a total of 272 points. We highlight our
experience-centred design methodology and key aspects of our system that
contributed to our competitiveness. We believe these aspects are crucial to
building robust and effective robotic systems.

本文介绍了 Cartman 自定义、经济实惠的笛卡尔机器人系统，在 Amazon Robotics Challenge 中获得第一名。该系统成功地完成了 14 次存储和 9 次取货，总得分达到了 272 分。我们强调了我们基于经验的设计方法和我们系统中的关键方面，这些方面对于构建强大和有效的机器人系统至关重要。