Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than $10\times$ speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.
翻译:深度学习在机器人感知方面取得了巨大的成功,但它的数据中心性质在面对不断变化的环境方面存在缺陷。相比之下,基于物理优化的方法具有更好的推广能力,但由于缺乏高层语义信息和依赖手动参数调整,它在复杂任务上的性能不如深度学习。为了利用这两个互补的世界,我们提出了 PyPose: 一个面向机器人的、基于 PyTorch 的库,将深度感知模型与基于物理优化相结合。PyPose 的架构整洁有序,具有命令式风格的接口,高效易用,易于集成到现实世界的机器人应用中。此外,它支持任意阶梯度的李群和李代数的并行计算、二阶优化器,如信任域方法。实验表明,与最先进的库相比,PyPose 的计算速度提高了10倍以上。为了促进未来的研究,我们提供了几个机器人学习领域的具体示例,包括SLAM、规划、控制和惯性导航。