minimum fast forward kinematics solver (+jacobian) in c++ and python binding
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Updated
Jun 15, 2024 - C++
minimum fast forward kinematics solver (+jacobian) in c++ and python binding
A differentiable physics engine and multibody dynamics library for control and robot learning.
Jacobian-Enhanced Neural Networks (JENN) are fully connected multi-layer perceptrons, whose training process was modified to account for gradient information. Specifically, the parameters are learned by minimizing the Least Squares Estimator (LSE), modified to minimize prediction error of both response values and partial derivatives.
Robot kinematics implemented in pytorch
A Julia interface to the C++ library ColPack for graph and sparse matrix coloring.
This project involves analyzing the stationary solutions of a system of differential equations depending on the parameter p4 .
A 6D differentiable underwater vehicle dynamics in body, ned and quaternion coordinate.
Rust numeric library with R, MATLAB & Python syntax
Inverse Kinematics of a 7dof manipulator
Robotics Toolbox for Python
Lightweight Python package for automatic differentiation
Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory
A modular C++17 framework for automatic differentiation
A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
Automatic Differentiation Library
Geometric and kinematic modeling of Hexapod Leg
Inverse Kinematics Solvers: Comprehensive Guide and Implementations
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