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One-Degree-of-Freedom System01:24

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A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor.

Tsige Tadesse Alemayoh1, Jae Hoon Lee1, Shingo Okamoto1

  • 1Department of Mechanical Engineering, Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama 790-8577, Ehime, Japan.

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Summary

This study presents a novel framework for biped robot control using deep learning and a single inertial measurement unit (IMU). It enables robot motion planning from minimal sensor data or user commands, achieving precise joint angle tracking.

Keywords:
deep learninginertial sensormotion synthesiswalking controller

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Area of Science:

  • Robotics
  • Machine Learning
  • Biomechanics

Background:

  • Biped robot motion control often requires complex sensor systems.
  • Human motion capture for robot control can be expensive and cumbersome.
  • Integrating human-like motion into robots is a key challenge in human-robot interaction.

Purpose of the Study:

  • To develop a minimalistic framework for biped robot motion control.
  • To enable robot motion synthesis from single inertial sensor data or user commands.
  • To achieve accurate biped robot locomotion mimicking human movement.

Main Methods:

  • Utilized a single inertial measurement unit (IMU) on a human subject for data collection.
  • Employed a Bi-LSTM encoder for human motion parameter estimation (velocity, gait phase).
  • Developed a feedforward motion generator-decoder network for synthesizing lower limb joint angles.
  • Integrated a Fourier series approach for generating motion parameters from user commands (speed, gait period).
  • Implemented constraint-consistent inverse dynamics control for biped robot walking control.
  • Validated the framework using MuJoCo physics engine simulations.

Main Results:

  • The framework successfully synthesized human-like motion parameters from IMU data and user commands.
  • The biped robot controller achieved a joint angle tracking error of ≤5°.
  • Demonstrated effective robot motion planning using minimal sensor input or simple user commands.

Conclusions:

  • The proposed framework offers an efficient and cost-effective solution for biped robot motion control.
  • Minimalistic sensing and user commands can effectively drive complex robotic locomotion.
  • This research provides a strong foundation for advanced human-robot interaction and control systems.