PDF-Robotics and Autonomous Systems Learning from demonstration and adaptation of biped PDF document

Our ultimate goal is to establish a design principle of a controller in order to achieve natural humanlike locomotion We suggest dynamical movement primitives as

Upload by marina-yarberry
Published on 2015-03-19

Presentation Embed Code

<iframe width="560" height="315" src="https://www.docslides.com/embed/47608" frameborder="0" allowfullscreen></iframe>

Download Presentation

Download Presentation The PPT/PDF document "Robotics and Autonomous Systems Learn..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Robotics and Autonomous Systems Learning from demonstration and adaptation of biped: Transcript

PDF-Robotics and Autonomous Systems Learning from demonstration and adaptation of biped PDF document

Presentation Embed Code

Download Presentation

Robotics and Autonomous Systems Learning from demonstration and adaptation of biped: Transcript

Related Contents

A Simple Reinforcement Learning Algorithm For Biped Walking Jun Morimoto Gordon Cheng

CRAM Cognitive Robot Abstract Machines Michael Beetz Gordon Cheng and Matthias Kranz

RI, NREC, and the Future

BIPED LOCOMOTION STABILITY ANALYSIS AND CONTROL Prahla

Victor Marmol School of Computer Science

Abstract Telerobotics actually result from the merger of two originally separate areas

Autonomy

Autonomous Robotics Sprinkler System

The M.S . Degree in Robotics Engineering

Secure Autonomous Vehicle Embedded Computing and Sensing

SIMBICON Simple Biped Locomotion Control KangKang Yin Kevin Loken Michiel van de Panne

Domain Adaptation with Structural Correspondence Learning

Autonomous/Assisted Driving

An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems