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Tutorial on how to get started with MuJoCo Simulation Platform. MuJoCo stands for Multi-Joint dynamics with Contact. It was acquired and made freely available by DeepMind in October 2021, and open …
Fleming, J.E., Senneff, S., Lowery, M.M. Multivariable closed-loop control of deep brain stimulation for Parkinson's disease, Journal of Neural Engineering, 20, 056029 (2023). https://doi.org/10.10…
Simple renderer for use with MuJoCo (>=2.1.2) Python Bindings.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Homepage for CSC417: Physics-based Animation
Decomposed Optimization Time Integration (DOT) is a domain-decomposed optimization method for fast, reliable simulation of deformation dynamics. DOT efficiently converges with frame-rate time-steps…
This program is coded using Python and uses two adaptive variable step-size integration methods (adaptive trapezoidal rule and adaptive Simpson's rule) to calculate the numerical integral value of …
A variable-step 4th order Runge Kutta integrator built for use with very large functions.
This repository contains the source code of the paper titled "Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning".
Calibrates the passive muscle forces in an OpenSim model based on experimentally-collected passive joint moments from Silder et al. 2007.
A tool to convert opensim 4.0+ MSK models into MuJoCo format with optimized muscle kinematics and kinetics
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling sa…
Particle swarm optimization (PSO) that supports constraints
Code used for parameter estimation with Geyer reflex model with additional COM velocity feedback
A bestiary of evolutionary, swarm and other metaphor-based algorithms
Evolutionary algorithm toolbox and framework with high performance for Python
A curated list of awesome Machine Learning frameworks, libraries and software.
A curated list of resources relevant to legged locomotion learning of robotics.
An obsidian template vault for tracking your academic life.
Repo for the paper: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. https://al.is.mpg.de/publications/wochneretal22
μ-Cuda, COVER THE LAST MILE OF CUDA. With features: intellisense-friendly, structured launch, automatic cuda graph generation and updating.
The mdbook source of a free online book on the theory and algorithms of physics-based simulations. You are welcome to make contributions by submitting pull requests or directly contacting the authors.
A Unified Framework for Rigid Body Dynamics, Master's thesis, Helmut Garstenauer, 2006
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2021 Spring
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases