A robot learning from demonstration framework that trains a recurrent neural network for autonomous task execution
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Updated
Nov 29, 2016 - C#
A robot learning from demonstration framework that trains a recurrent neural network for autonomous task execution
Objectworld Experiments
Train a robot to see the environment and autonomously perform different tasks
A framework that focuses on using bayesian and Dynamic Bayesian Networks to perform Learning from observation on Discrete Domains
C++ libarary for executing JT-DS
Augmented Joint-space Task-oriented Dynamical Systems
CNN models to classify the observations of a social greeting behavioral intervention
Interface between a DBN model and CNN models to learn from demonstrations
Temporal Context Graph - Interval-based temporal reasoning model capable of learning tasks with cyclical events
YODO: Inverse Reinforcement Learning
Combined Learning from Demonstration and Motion Planning
Learning second order dynamical system
Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support.
Dynamic Motion Primitives
Stable dynamical system learning using Euclideanizing flows
Implementation of the paper "Human-like Planning for Reaching in Cluttered Environments" (ICRA 2020)
Inverse optimal control from incomplete trajectory observations, proposing the concept of the recovery matrix which provides further insights into objective learning process.
Implementation of Inverse Reinforcement Learning Algorithm on a toy car in a 2D world problem, (Apprenticeship Learning via Inverse Reinforcement Learning Abbeel & Ng, 2004)
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