Nonparametric Representations for Integrated Inference, Control, and Sensing

Abstract

The objective of this research program was to develop mathematical foundations of information gathering through an integrated theory of sensing, inference, and control. The goal of the team was to develop a new framework for autonomous operations that will extend the state of the art in distributed learning and modeling from data, and tightly integrate these models into new decentralized cooperative planning algorithms. The main output of this effort will be a fundamental theory to integrate decentralized information driven planning methods for heterogenous teams with nonparametric Bayesian models of uncertainty. The feasibility and aspects of the value of the theory were demonstrated via integrated software and hardware experiments.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2015
Accession Number
ADA627279

Entities

People

  • Andreas Krause
  • John Fisher
  • Jon How
  • Luis Galup
  • Stefano Soatto
  • Trevor Darrell

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Human Systems
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Bayesian Networks
  • Computer Languages
  • Computer Vision
  • Data Processing
  • Databases
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems