论文标题
在机器人和云之间进行持续学习的培训数据
Sampling Training Data for Continual Learning Between Robots and the Cloud
论文作者
论文摘要
当今的机器人舰队越来越多地测量大量的视频和LiDAR感觉流，这些视频可以用于有价值的训练数据，例如公路建设地点的罕见场景，以稳步改善机器人感知模型。但是，重新训练感知模型对中央计算服务器（或“云”）中不断增长的丰富感官数据的增长模型给网络传输，云存储，人类注释和云计算资源带来了巨大的时间和成本负担。因此，我们引入了Harvestnet，这是一种智能抽样算法，它仅通过存储罕见的，有用的事件来稳步改善在云中重新训练的感知模型，从而驻留在机器人上并减少系统瓶颈。 HarvestNet显着提高了我们新颖的道路构造站点数据集，自动驾驶汽车的现场测试以及流面识别的现场测试，同时减少云存储，数据集注释时间，并且云计算时间介于65.7-81.3％之间。此外，它比基线算法更准确的1.05-2.58倍，并且在嵌入式深度学习硬件上可伸缩。我们为Google Edge Tensor处理单元（TPU），扩展技术报告和一个新颖的视频数据集提供了一套计算效率感知模型，网址为https://sites.google.com/view/harvestnet。
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models. However, re-training perception models on growing volumes of rich sensory data in central compute servers (or the "cloud") places an enormous time and cost burden on network transfer, cloud storage, human annotation, and cloud computing resources. Hence, we introduce HarvestNet, an intelligent sampling algorithm that resides on-board a robot and reduces system bottlenecks by only storing rare, useful events to steadily improve perception models re-trained in the cloud. HarvestNet significantly improves the accuracy of machine-learning models on our novel dataset of road construction sites, field testing of self-driving cars, and streaming face recognition, while reducing cloud storage, dataset annotation time, and cloud compute time by between 65.7-81.3%. Further, it is between 1.05-2.58x more accurate than baseline algorithms and scalably runs on embedded deep learning hardware. We provide a suite of compute-efficient perception models for the Google Edge Tensor Processing Unit (TPU), an extended technical report, and a novel video dataset to the research community at https://sites.google.com/view/harvestnet.
