jacobian
There are 88 repositories under jacobian topic.
petercorke/robotics-toolbox-python
Robotics Toolbox for Python
petercorke/robotics-toolbox-matlab
Robotics Toolbox for MATLAB
Axect/Peroxide
Rust numeric library with high performance and friendly syntax
UM-ARM-Lab/pytorch_kinematics
Robot kinematics implemented in pytorch
jhavl/dkt
A Tutorial on Manipulator Differential Kinematics
YashBansod/Robotics-Planning-Dynamics-and-Control
RPDC : This contains all my MATLAB codes for the Robotics, Planning, Dynamics and Control . The implementations model various kinds of manipulators and mobile robots for position control, trajectory planning and path planning problems.
gisbi-kim/nano-pgo
For an education purpose, from-scratch, single-file, python-only pose-graph optimization implementation
tfjgeorge/nngeometry
{KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch
Sarrasor/RoboticManipulators
Calculation of forward and inverse kinematics, Jacobian matrices, dynamic modeling, trajectory planning and geometric calibration for robotic manipulators
ami-iit/jaxsim
A differentiable physics engine and multibody dynamics library for control and robot learning.
gradientpm/gradient-mts
A collection of gradient-domain light transport algorithms implemented with Mitsuba
shantanu1109/Coursera-Imperial-College-of-London-Mathematics-For-Machine-Learning-Specialization
This Repository contains Solutions to the Quizes & Lab Assignments of the Mathematics for Machine Learning Specialization offered by Imperial College of London on Coursera taught by David Dye, Samuel J. Cooper, A. Freddie Page, Marc Peter Deisenroth
SciML/ParameterizedFunctions.jl
A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
ivanalberico/Robot-Dynamics-ETH
Weekly MATLAB exercises of the course "Robot Dynamics", ETH Zürich (Fall 2020).
SciML/SparsityDetection.jl
Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)
mfschubert/sparsejac
Efficient forward- and reverse-mode sparse Jacobians using Jax
eguidotti/calculus
High Dimensional Numerical and Symbolic Calculus in R
gradientpm/gvpm
Gradient-domain Volumetric Photon Density Estimation, SIGGRAPH 2018
martinjrobins/diffsol
ODE solver library in Rust
Walid-khaled/7DOF-KUKA-Linear-Axis-Forward-and-Inverse-Kinematics
In this repository, the implementation of forward and inverse kinematics by redundancy resolution is presented for KUKA on linear axis 7-DOF robot. The Redundancy Resolution includes three methods, which are Jacobian-based (Damped Least Square and Weighted Pseudoinverse), Null Space, and Task Augmentation.
adrhill/SparseConnectivityTracer.jl
Fast operator-overloading Jacobian & Hessian sparsity detection.
Kartik17/Robotic_Arm
Forward and Inverse Kinematics for Robotic Manipulator
m-pilia/disptools
Generate displacement fields with known volume changes
GinoAvanzini/iiwa-kinematics
Kinematic analysis of KUKA LBR iiwa 7DOF manipulator
HiroIshida/tinyfk
small fast forward kinematics solver (+jacobian) in c++ and python binding
SpehleonLP/IK-Guide
Inverse Kinematics Solvers: Comprehensive Guide and Implementations
vvv-school/tutorial_inverse-kinematics
Tutorial on Inverse Kinematics
diogoalmeida/asymmetric_manipulation
Code for implementing and experimenting with cooperative manipulation controllers. Includes a controller based on a novel extended relative Jacobian formulation.
RSuryaNarayan/CEMA
Chemical Explosive Mode Analysis for computational/experimental combustion diagnostics using Julia SciML features
djmaxus/autodj
Automatic Differentiation Library
edxmorgan/diff_uv
A differentiable underwater vehicle dynamics in body and ned(euler & quaternion).
alifahrri/kinetools
Jacobian Inverse Kinematic using Automatic Differentiation
PyNumAl/Python-Numerical-Analysis
Implementation of Numerical Analysis algorithms/methods in Python
shb84/JENN
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.
auralius/numerical-jacobian
Calculate jacobian numerically at a given condition
exanauts/ColPack.jl
A Julia interface to the C++ library ColPack for graph and sparse matrix coloring.