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Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural

Ajaz A Bhat1, Vishwanathan Mohan2

  • 11School of Psychology, University of East Anglia, Norwich, UK.

Cognitive Computation
|August 28, 2018
PubMed
Summary

This study introduces a bioinspired neural architecture enabling two robots to cooperate in complex assembly tasks by learning internal models of their bodies and workspace. This allows for predictive action planning and successful task completion, even in unstructured environments.

Keywords:
Body schemaCooperationIndustrial assemblyInternal modelsPeripersonal spacePlanningSpatial reasoning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Neuroscience

Background:

  • Cooperative goal-directed behavior in shared workspaces is crucial for human and robotic activities.
  • Anticipating actions and environmental context is key for successful collaboration.
  • Existing industrial automation often lacks the adaptability for complex, unstructured tasks.

Purpose of the Study:

  • To develop a bioinspired neural architecture for goal-directed action planning in multi-robot systems.
  • To enable robots to learn and utilize internal models of their bodies and peripersonal space for cooperation.
  • To demonstrate the framework's effectiveness in a real-world industrial assembly task.

Main Methods:

  • A bioinspired neural architecture based on coupled internal models of robot body and peripersonal space was developed.
  • Internal models were learned jointly through sensorimotor exploration.
  • Reward-based dynamics were used for planning and anticipating actions in a dynamic assembly scenario.

Main Results:

  • The architecture enabled two robots to jointly assemble industrial fuse-boxes in a spatially unstructured environment.
  • Robots successfully planned collision-free trajectories and cooperated on tasks not feasible individually.
  • The framework demonstrated effective anticipation and action sequencing for maximizing assemblies.

Conclusions:

  • The proposed neural framework facilitates sophisticated goal-directed action planning and cooperation in multi-robot systems.
  • The approach offers a computational perspective on neuroscientific findings regarding body schema and peripersonal space.
  • The domain-agnostic nature of the framework suggests broad applicability to various robotic tasks and platforms.