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Adaptive Prior Selection for Repertoire-Based Online Adaptation in Robotics.

Rituraj Kaushik1, Pierre Desreumaux1, Jean-Baptiste Mouret1

  • 1Inria, CNRS, Université de Lorraine, Nancy, France.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
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This study introduces Adaptive Prior selection for Repertoire-based Online Learning (APROL), a novel algorithm for robot adaptation. APROL efficiently selects appropriate policies from multiple repertoires, outperforming existing methods in challenging robotic tasks.

Area of Science:

  • Robotics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Repertoire-based learning offers data-efficient robot adaptation by selecting pre-learned policies.
  • Previous methods assumed a single repertoire is sufficient, limiting adaptation to diverse situations.

Purpose of the Study:

  • To develop a more robust adaptation method by generating and selecting from multiple situation-specific repertoires.
  • To introduce the Adaptive Prior selection for Repertoire-based Online Learning (APROL) algorithm for planning actions with unknown situational priors.

Main Methods:

  • Generated multiple repertoires for various robot situations (e.g., damage, different environments).
  • Developed APROL to select the most relevant prior policy based on the current situation.
  • Evaluated APROL on simulated robotic arm object pushing and hexapod robot goal reaching tasks.
Keywords:
data-efficient robot learningevolutionary roboticsfault tolerance in roboticsmodel-based learningrepertoire-based robot learning

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  • Compared APROL against Reset-free Trial and Error (RTE) and single-repertoire baselines.

    • Main Results:

    • APROL demonstrated superior performance by solving tasks in less interaction time compared to baselines.
    • Successfully adapted a real, damaged hexapod robot to reach a goal while avoiding obstacles.
    • Showcased the algorithm's ability to learn compensatory policies for effective online adaptation.

    Conclusions:

    • APROL significantly enhances repertoire-based learning by utilizing multiple situation-specific repertoires.
    • The algorithm provides a more effective and data-efficient approach for robot adaptation in unknown or changing environments.
    • APROL's successful real-world demonstration highlights its practical applicability for robust robotic systems.