Awesome

Robot gains social intelligence through Multimodal Deep Reinforcement Learning

This project proivdes the implementation of Multimodal Deep Q-Network (MDQN) using which the robot acquired social interaction skills after 14 days of interaction with people in an uncontrolled realworld.

Requirements:

1- Pepper Robot (https://www.ald.softbankrobotics.com/en/cool-robots/pepper)

2- Mbed microcontroller (https://developer.mbed.org/platforms/mbed-LPC1768/)

3- FSR Touch sensor (https://www.sparkfun.com/products/9376)

Procedure:

1- Bring FSR into action through integration with mbed or any microcontroller (please burn the code 'uC.cpp' on mbed).

2- Paste FSR on Pepper's right hand (as shown in fig 1 in [1]).

3- Run mbed_usb.py, robot_listen.py and train_ql.lua on three seperate terminals for data_generation_phase.

4- Run train_ql.lua only for learning_phase.

[1] Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa and Hiroshi Ishiguro, "Robot gains social intelligence through Multimodal Deep Reinforcement Learning", Proceedings of IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 745-751, Cancun, Mexico 2016.