Implicit Communication in Human-Machine Collaboration

Abstract

The goal of this project was to advance human-machine collaboration, specifically focusing on the machine's ability to understand and partake in implicit communication. We focused much of the work on inferring underlying human objectives and preferences, from learning from different types of input (physical corrections, feature traces, even one single state), to introducing better models of human behavior that lead to better inference, to preventing wrong/harmful inference. We also made a number of contributions on enabling robots to communicate themselves implicitly: objectives, capability/incapability, their future task plans, and even emotional state. We have published numerous conference and journal papers, and received 4 best paper nominations (three times at HRI, the premier conference inhuman-robot interaction, and once at TRO).

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 10, 2022
Accession Number
AD1190026

Entities

People

  • Anca Dragan

Organizations

  • University of California Regents

Tags

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • California
  • Computer Science
  • Environment
  • Human Behavior
  • Human-Machine Interfaces
  • Human-Machine Systems
  • Human-Robot Interaction
  • Information Processing
  • Information Systems
  • Learning
  • Models
  • Probabilistic Models
  • Reinforcement Learning
  • Robotics
  • Robots
  • Scientific Research

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • Autonomy
  • Autonomy - Human-Robot Interaction