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Toward autonomous event-based sensorimotor control with supervised gait learning and obstacle avoidance for robot

Shahin Hashemkhani1, Vijay Shankaran Vivekanand1, Samarth Chopra1

  • 1Department of Electrical and Computer Engineering (ECE), Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United States.

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Summary

This study introduces a bio-inspired framework for autonomous miniature robots, enabling adaptive navigation in challenging environments using event-based sensorimotor control and a hierarchical system. The robots learn gaits for obstacle avoidance under resource constraints.

Keywords:
central pattern generatorcoupled neural networksdynamic state machinemulti-timescale feedbacksensorimotor controlspike-time dependent plasticity

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Area of Science:

  • Robotics
  • Neuroscience
  • Artificial Intelligence

Background:

  • Miniature robots require autonomous navigation for disaster response and accessing hazardous areas.
  • Resource constraints (compute, storage, power) limit unsupervised operation in edge robotics.
  • Event-based sensorimotor control offers adaptive navigation and real-time decision-making.

Purpose of the Study:

  • To present a novel bio-inspired hierarchical control framework for autonomous miniature robot navigation.
  • To address limitations of resource constraints and environmental variability in robot control.
  • To enable adaptive gait learning and real-time obstacle avoidance.

Main Methods:

  • Utilized a tunable multi-layer neural network with a hardware-friendly Central Pattern Generator (CPG) for motion timing.
  • Implemented a Dynamic State Machine (DSM) for hierarchical autonomous operation and adaptability.
  • Employed a nonlinear neuron model with mixed feedback for rhythmic motor control patterns.
  • Applied supervised Spike-Time Dependent Plasticity (STDP) for autonomous gait learning (walk, crawl).

Main Results:

  • Demonstrated autonomous gait learning and state transitions on the Petoi robot platform.
  • Showcased real-time obstacle avoidance capabilities managed by the DSM.
  • Achieved adaptive navigation in uneven terrains and response to environmental fluctuations.
  • Presented a comprehensive analysis of the framework's architecture and network equations.

Conclusions:

  • The proposed bio-inspired framework enables robust, adaptive, and autonomous navigation for miniature robots under severe resource constraints.
  • The hierarchical control system, combining CPG and DSM, effectively manages complex behaviors like gait learning and obstacle avoidance.
  • This approach advances edge robotics for applications requiring intelligent, self-sufficient mobile agents in unpredictable environments.