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

• 64-bit Ubuntu 14.04 or later
• Python 2.7
• check detailed requirements file

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]