PyPose: A Library for Robot Learning with Physics-based Optimization
| Autor: | Wang, Chen, Gao, Dasong, Xu, Kuan, Geng, Junyi, Hu, Yaoyu, Qiu, Yuheng, Li, Bowen, Yang, Fan, Moon, Brady, Pandey, Abhinav, Aryan, Xu, Jiahe, Wu, Tianhao, He, Haonan, Huang, Daning, Ren, Zhongqiang, Zhao, Shibo, Fu, Taimeng, Reddy, Pranay, Lin, Xiao, Wang, Wenshan, Shi, Jingnan, Talak, Rajat, Cao, Kun, Du, Yi, Wang, Han, Yu, Huai, Wang, Shanzhao, Chen, Siyu, Kashyap, Ananth, Bandaru, Rohan, Dantu, Karthik, Wu, Jiajun, Xie, Lihua, Carlone, Luca, Hutter, Marco, Scherer, Sebastian |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Project Website: https://pypose.org Documentation: https://pypose.org/docs/ Tutorial: https://pypose.org/tutorials/ Source code: https://github.com/pypose/pypose |
| Databáze: | arXiv |
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