A distributed and morphology-independent strategy for adaptive locomotion in self-reconfigurable modular robots

David Johan Christensen, Ulrik Pagh Schultz, Kasper Støy

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In this paper, we present a distributed reinforcement learning strategy for morphology-independent life-long gait learning for modular robots. All modules run identical controllers that locally and independently optimize their action selection based on the robot's velocity as a global, shared reward signal. We evaluate the strategy experimentally mainly on simulated, but also on physical, modular robots. We find that the strategy: (i) for six of seven configurations (3-12 modules) converge in 96% of the trials to the best known action-based gaits within 15 min, on average, (ii) can be transferred to physical robots with a comparable performance, (iii) can be applied to learn simple gait control tables for both M-TRAN and ATRON robots, (iv) enables an 8-module robot to adapt to faults and changes in its morphology, and (v) can learn gaits for up to 60 module robots but a divergence effect becomes substantial from 20-30 modules. These experiments demonstrate the advantages of a distributed learning strategy for modular robots, such as simplicity in implementation, low resource requirements, morphology independence, reconfigurability, and fault tolerance.

Original languageEnglish
JournalRobotics and Autonomous Systems
Volume61
Issue number9
Pages (from-to)1021-1035
ISSN0921-8890
DOIs
Publication statusPublished - 2013

Keywords

  • Distributed control
  • Fault tolerance
  • Locomotion
  • Online learning
  • Self-reconfigurable modular robots

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