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Interactive Learning and Adaptation for the Sequence Learning task with NAO

  • Data Collection, Modeling and Analysis
  • Interactive Reinforcement Learning for Robot Learning
  • Dynamic User Modeling
  • EEG engagement monitoring using MUSE (Learning from Feedback)
  • Online GUI Robot Learning (Learning from Guidance)
  • Interactive Learning and Adaptation Framework - User Studies

Requirements

Running instructions

  • Run muse-io muse-io --device Muse-XXXX --osc osc.udp://localhost:5000
  • Run play.py (make sure the port number is the same in play.py and muse_pyliblo_server.py files -- TODO: create launch file to run these automatically)

Note: for the purposes of the game, we have built a buzzer-like box with EASY(R) buttons for the user to respond, responses can be also recorder through keyboard

output files

  • MUSE output files
    During the interaction, we collect MUSE data (1) when the robot announces the sequence and (2) when the user reponds

  • Each line on the file starts with a character, each with a specific meaning (check http://developer.choosemuse.com/research-tools/available-data for reference):
    h - receive horseshoe - status indicator values
    eeg – raw EEG Data
    a – Alpha relative
    b – Beta relative
    g – Gamma relative
    d – Delta relative
    t – Theta relative
    Aa – Alpha absolute
    Ab – Beta absolute
    Ag – Gamma absolute
    Ad – Delta absolute
    At – Theta absolute
    as – Alpha session score Session score info
    bs – Beta session score
    gs – Gamma session score
    ds – Delta session score
    ts – theta session score
    c – concentration
    Each line has four readings from sensors in left ear, left forehead, right forehead, right ear.

  • Robot_#
    This file records data from Muse when user is listening to the robot while it is announcing the sequence

  • User_#
    This file records data from Muse when user is responding by pressing the buttons

  • logfile
    For each round the following details are recorded:
    Turn number, length of sequence, robot feedback, current score, success (1) / failure (-1), reaction time, completion time, sequence given by robot, sequence entered by user.
    Reaction time: Time until user enters the first character in the sequence.
    Completion time: Time until user completes the entire sequence.

  • state_EEG -- state formulation for the RL
    In each round, the below details are recorded:
    Sequence length (3,5,7,9), robot feedback (0: none, 1: positive, 2: negativ), previous score [-4, 4], corresponding EEG filenames
    Score is calculated by the formula: (result) x (difficulty_level), where result = [-1, 1] and difficulty_level = [1,2,3,4]