Designing Collaborator Robots for Highly-Dynamic Multi-Human, Multi-Robot Teams

Abstract

In the proposed research the performer will develop a unified architecture for robot teamwork, which incorporates novel perceptual, control, social behavior and learning systems that will provide robots with awareness, flexibility, sociability and adaptability during long-term interactions in multi-human, multi-robot teams. The problem: In this project we propose to address the problem of designing collaborator robots that can operate effectively as members of multi-human, multi-robot teams. The solutions designed for this purpose would be applicable to multiple application domains: military (groups of human soldiers and robots), manufacturing (mixed human-robot assembly environments) or service robotics (humans and robots co-existing in physical worlds). The heterogeneous, multi-agent team poses different challenges from human-robot collaboration in single human-robot domains. In particular, successful interaction and collaboration among the agents of a group that work in close proximity requires several key capabilities: i) Awareness: the agents need to be continuously aware of their surroundings (e.g., what are the agent’s intentions, what types of interactions they are currently engaged in (who is working with whom, who is speaking to whom, etc.), ii) Flexibility in control: the agents should be prepared to engage in multiple interactions while switching back and forth between different tasks and roles (e.g., subordinate, peer, supervisor), iii) Sociability: the agents should be able to communicate using verbal cues, to either receive direct commands or instructions from human users, or to express their own goals/intentions to the human collaborators, and iv) Adaptability: the agents should remember and learn from their past experiences, in a lifelong learning process. The research goal of this project is to design a unified architecture that integrates the above capabilities to sustain long-term, real-world robot teamwork.

Document Details

Document Type
DoD Grant Award
Publication Date
Nov 23, 2016
Source ID
N000141612312

Entities

People

  • Monica Nicolescu

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Nevada, Reno

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - Human-Robot Interaction