malteschilling

ddrl_hexapod

ddrl

Deep Decentralized Reinforcement Learning - locally structured architecture for DRL in a continuous locomotion control task.

temporalConvolutionalNetwork

Implementation of a Temporal Convolutional Network in Keras - it is tested on a classification and regression task.

hierarchicalQuaternionMMC

Implementation of a hierarchical Mean of Multiple Computation neural network in Python. The body model is based on an axis-angle representation of joint movements using dual quaternions. It is shown in detail how the network provides a solution for the forward kinematic problem applied for the case of a seven degrees of freedom robot manipulator. Furthermore, it is used in a complex scenario of a bimanual movement task. This demonstrates how the MMC approach can be easily scaled up from a representation of a single arm to a complex model of a complete body.

AdvancedML

Course on advanced Machine Learning. (html) Slides for the lecture.

advml_binder

arob_notebooks

cognitiveWalker

Cognitive system for the control of a hexapod robot.

drl_seminar

Deep Reinforcement Learning Course 2017 - includes basic python implementations of (deep) reinforcement learning algorithm.

hector

ODE Simulation and Visualization of the six-legged Hector robot.

pipe_project

joint_mappings_nn

Learning of a mapping between different legs - given as joint angles from one (target) leg and corresponding joint angles for the posterior leg (aiming at this position).

LearningPredictiveEncoderDecoder

Learning Predictive Encoder-Decoder for falling Object images.

Lehrprobe_MS_AnalyseErwarteterFall

logseq

This is simply to publish my templates to the world :)

malteschilling.github.io

normalizationMMC

Implementation of a functional internal body model that is based on the Mean of Multiple Computation principle in Python. The model is completely realized in a recurrent neural network as necessary normalization steps are integrated into the neural model itself. Furthermore, a dynamic extension is applied to the model.

PIPECore

Core libraries for building and editing Petri nets

pyESN

Echo State Networks in Python

robust_ddrl

Experiments on Robustness of Decentralized DRL approach wrt. variation of hyper parameters of Neural Networks.