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领域取得了显著成功，但其以数据为中心的特性在适应不断变化的环境时存在局限性。相比之下，基于物理的优化方法泛化能力更强，但由于缺乏高层语义信息且依赖人工参数调优，在复杂任务中表现不佳。为融合这两种互补方法的优势，我们提出PyPose：一款面向机器人学、基于PyTorch的库，将深度感知模型与物理优化相结合。PyPose架构清晰有序，采用命令式风格接口，兼具高效性与易用性，便于集成至实际机器人应用中。此外，它支持李群与李代数任意阶梯度的并行计算及二阶优化器（如信赖域方法）。实验表明，PyPose的计算速度相比现有最优库提升超过10倍。为促进未来研究，我们提供了机器人学习多个领域（包括SLAM、规划、控制与惯性导航）的具体应用示例。