Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

Abstract

The modular snake robots in Carnegie Mellons Biorobotics lab provide an intriguing platform for research: they have already been shown to excel at a variety of locomotive tasks and have incredible potential for navigating complex terrains, but much of that potential remains untapped. Unfortunately, many techniques commonly used in robotics prove inapplicable to these snake robots. This is because of the robots complex, multi-modal locomotion dynamics, which are difficult to model, and their small size and frequent impacts, which preclude addition of many standard sensors.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2013
Accession Number
ADA623569

Entities

People

  • Matthew Tesch

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Bayesian Networks
  • Climate Change
  • Computational Fluid Dynamics
  • Computational Science
  • Gaussian Distributions
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Machine Learning
  • Mathematical Filters
  • Monte Carlo Method
  • Probabilistic Models
  • Probability Distributions
  • Random Variables
  • Surveys
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy