OpenDR：一个开放式工具包，用于实现机器人的高性能、低占用空间深度学习
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
摘要：
现有的深度学习（DL）框架通常不为机器人提供现成的解决方案，其中存在非常具体的学习、推理和实施问题。与传统方法相比，它们相对陡峭的学习曲线和DL采用的不同方法，以及DL模型的高复杂性，这通常导致需要采用专用硬件加速器，进一步增加了在机器人中采用DL模型所需的工作量和成本。此外，大多数现有的DL方法遵循静态推理范式，如传统的计算机视觉管道所继承的，忽略了主动感知，主动感知可以用于主动地与环境交互以提高感知准确性。在本文中，我们介绍了机器人开放式深度学习工具包（OpenDR）。OpenDR旨在开发一个开放的、非专有的、高效的、模块化的工具包，机器人公司和研究机构可以轻松使用该工具包来高效地开发和部署人工智能和认知技术，以解决上述挑战。我们还详细介绍了设计选择，以及为克服这些挑战而创建的抽象界面。该接口可以描述各种机器人任务，超越现有框架已知的传统DL认知和推理，结合开放性，同质性和面向机器人的感知，例如，作为其核心设计原则。
英文摘要：
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
