PyPose: A Library for Robot Learning with Physics-based Optimization

Abstract

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.

Project Website: https://pypose.org Documentation: https://pypose.org/docs/ Tutorial: https://pypose.org/tutorials/ Source code: https://github.com/pypose/pypose

References in corpus (7)

• TLIO: Tight Learned Inertial Odometry
• Theseus: A Library for Differentiable Nonlinear Optimization
• Deep Patch Visual Odometry
• Present and Future of SLAM in Extreme Underground Environments
• AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
• Sparse Pose Graph Optimization in Cycle Space
• Estimating High Order Gradients of the Data Distribution by Denoising

Cited by in corpus (8)

• Autonomous Drone Racing: A Survey
• PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
• iPlanner: Imperative Path Planning
• iSLAM: Imperative SLAM
• AirLine: Efficient Learnable Line Detection with Local Edge Voting
• PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
• iKap: Kinematics-aware Planning with Imperative Learning
• iWalker: Imperative Visual Planning for Walking Humanoid Robot