Autonomous Robot Dancing

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Published on February 16, 2014

Author: saharseifzadeh

Source: slideshare.net

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Autonomous Robot Dancing Driven by Beats and Emotions of Music- Nao

((in the name of the best creator)) Distributed Artificial Intelligence Course AUTONOMOUS ROBOT DANCING DRIVEN BY BEATS AND EMOTIONS OF MUSIC

INTRODUCTION Many robot dances are preprogrammed by choreographers.  choreography: OUT OF 15  2

 primitive emotions  key frame (static poses) OUT OF 15 Our work is made up of two parts: (1) The first algorithm plans a sequence of dance movements that is driven by the beats and the emotions detected through the preprocessing of selected dance music. (2) We also contribute a real-time synchronizing algorithm to minimize the error between the execution of the motions and the plan. 3

OUT OF 15 WE CREATE A LARGE LIBRARY OF MOTION PRIMITIVES, BY DIVIDING THE JOINTS OF THE NAO HUMANOID ROBOT INTO 4 CATEGORIES, WHERE EACH CATEGORY OF JOINTS CAN ACTUATE INDEPENDENTLY. 4

PUAL EKMAN PROPOSED 6 PRIMARY EMOTIONS:      OUT OF 15  1-happy 2-sad 3-surprised 4-angry 5-fear 6-disgust 5

JOINTS: 1. Head (Head): HeadYaw, HeadPitch  2. Left Arm (LArm): LShoulderPitch, LShoulderRoll, LElbowYaw, LElbowRoll       OUT OF 15  3. Right Arm (RArm): RShoulderPitch, RShoulderRoll, RElbowYaw, RElbowRoll 4. Legs (Legs): LHipYawPitch, LHipRoll, LHipPitch, LKneePitch, LAnklePitch, LAnkleRoll, RHipRoll, RHipPitch, RKneePitch, RAnklePitch, RAnkleRoll 6

  a and v is between -1 , 1 super vector regression (a , v)  94% agreement emotion presentation:  SMERS (SVR) OUT OF 15 emotion extraction: 7

 OUT OF 15 beat tracking beat tracking+ autocorrelation analysis+ neural network amplitude ( the best candidate) 8

MAPPING MOTION PRIMITIVE TO ACTIVATIONVALENCE SPACE FROM STATIC POSTURES DATA  OUT OF 15 We collected 4 static postures of the NAO humanoid robot for each of Ekman's 6 basic emotions: Happy, Sad, Angry, Surprised, Fear and Disgust. we have a total of 24 emotional static postures. 6*4=24 9

OUT OF 15 10

EMOTION FOR NEXT MOTION PRIMITIVE OUT OF 15 Motion primitives are selected sequentially and stretched to all a whole number of beat times. To choose the next motion primitive, we need the emotion at the end of the previous motion primitive. We simply estimate the emotion at each beat time by linearly interpolating the (a; v) values 11

THE MARKOV DANCER MODEL primitive motions:  (i) be continuous  (ii) reflect the musical emotion  OUT OF 15  (iii) be interestingly non-deterministic exclusion we use an adaptive real-time synchronizing algorithm 12

OUT OF 15 13

CONCLUSION OUT OF 15 We show that we can automate robot dancing by forming schedules of motion primitives that are driven by the emotions and the beats of any music on a NAO humanoid robot. The algorithms are general and can be used on any robot. From emotion labels given for static postures, we can estimate the activation-valence space locations of the motion primitives and select the appropriate motion primitives for emotions detected in music. 14

THE END OUT OF 15 15

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