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CONTRÔLEURS
NEURONAUX PLASTIQUES
POUR L'ÉMERGENCE DE
COORDINATIONS
MOTRICES DANS
L'INTERACTION PHYSIQUE
ET SOCIALE
HUMAIN/...
1. INTRODUCTION
• Human rhythmic movements
• Interpersonal coordination
• Synchrony and synchronization
2. RHYTHMIC MOVEME...
INTRODUCTION
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 3
HUMAN PHYSICAL CO-MANIPULATION FOR
RYHTHMIC TASKS
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 4
Italian Institute of Technolo...
HUMAN MOVEMENTS
• Discrete movements :
• Static (taking object)
• Dynamic (sport, basket)
AVRIL 19 - IMT - P. HÉNAFF - MIN...
MAIN PROPERTIES IN INTERPERSONAL INTERACTION
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Involuntary interpersonal coordinati...
WHAT IS SYNCHRONIZATION OF MOVEMENTS?
Interpersonal and Interlimb Coordination
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 7
...
Mechanical
system
SYNCHRONIZATION OF DYNAMIC SYSTEMS
8AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Simulated
biological
system...
EXAMPLE OF INTERPERSONAL SYNCHRONIZATION IN
PHYSICAL INTERACTION : THE HANDSHAKING
Phase 1 – SoH
Start of Handshake
Phase ...
[Zehr, 2006]
Rhythmic or discrete
movements?
[McCrea & Rybak, 2008]
HUMAN RHYTHMIC MOVEMENTS ARE CONTROLLED
BY CENTRAL PAT...
COMPUTATIONAL MODEL OF CPG ?
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 11
Rhythmic level
Formation level
motor level
COMPUTATIONAL MODEL OF CPG ?
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
CPG for l...
MODEL OF RHYTHMIC NEURON :
13AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Neuron model = Non-linear oscillator (Rowat & Selver...
Input signal
CPG MODEL FOR ONE JOINT
 Rhythmic generator cells : Rowat-Selverston model (Van der Pol formalism):
 Intern...
LEARNING MOTOR COORDINATION
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 15
WAVING BACK EXAMPLE
• Human waves at robot
• Optical flow of the hand detected
• Robot waves back and adapts to the human ...
WAVING BACK : CONTROL ARCHITECTURE
 2 joints controlled :
 CPG1 input (shoulder) : optical flow
 CPG2 input (elbow): ou...
WAVING BACK EXPERIMENT
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
18
Synchronization between joints and optical flow Hebbian...
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
19
WAVING BACK EXPERIMENT
plasticity influences learning speed
with amplitude and...
COORDINATED COMPLEX MOVEMENTS
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 20
shoulder
elbow
shoulder
Circles at different spe...
Can CPGs achieve both rhythmic
and discrete movements ?
21AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
OSCILLATING NEURON CAN BEHAVE AS A PID
CONTROLLER
22AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Non oscillating :
EXPERIMENT
23
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Joint
phase
portrait
CPG
phase
portrait
 Robotic arm coupled to a ...
RESULTS
CPG can transition smoothly between both states by
changing a single parameter
24
AVRIL 19 - IMT - P. HÉNAFF - MIN...
CONCLUSION & FUTURE WORKS
 versatility and reliability of CPG controller that can adapt to varying
frequencies
 dynamic ...
AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
Thanks for your attention…
26
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Colloque IMT -04/04/2019- L'IA au cœur des mutations industrielles - Contrôleurs neuronaux plastiques pour l'émergence de coordinations motrices dans l'interaction physique et sociale humain/robot

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Colloque IMT -04/04/2019- L'IA au cœur des mutations industrielles - Session Robotique, Perception, Interaction: Contrôleurs neuronaux plastiques pour l'émergence de coordinations motrices dans l'interaction physique et sociale humain/robot. Présentation par Patrick Henaff (IMT Mines Nancy)

Published in: Engineering
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Colloque IMT -04/04/2019- L'IA au cœur des mutations industrielles - Contrôleurs neuronaux plastiques pour l'émergence de coordinations motrices dans l'interaction physique et sociale humain/robot

  1. 1. CONTRÔLEURS NEURONAUX PLASTIQUES POUR L'ÉMERGENCE DE COORDINATIONS MOTRICES DANS L'INTERACTION PHYSIQUE ET SOCIALE HUMAIN/ROBOT Patrick Hénaff Mines Nancy LORIA AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 1
  2. 2. 1. INTRODUCTION • Human rhythmic movements • Interpersonal coordination • Synchrony and synchronization 2. RHYTHMIC MOVEMENTS OF ROBOTS • Central pattern generators (CPG) • Computational model of CPG 3. LEARNING MOTOR COORDINATION • Implementation of the bio-inspired controller • Experiments: • Waving back with a robot • Coordination of complex rhythmic movements • Discrete movements 4. CONCLUSION & FUTURE WORKS OUTLINE AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 2
  3. 3. INTRODUCTION AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 3
  4. 4. HUMAN PHYSICAL CO-MANIPULATION FOR RYHTHMIC TASKS AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 4 Italian Institute of Technology, Dep of Advanced robotics, 2016 • Rhythmics tasks are often base on regular motions without need of accurate trajectories • Control of antropomorphic robots can be inspired from human motor nervous system?
  5. 5. HUMAN MOVEMENTS • Discrete movements : • Static (taking object) • Dynamic (sport, basket) AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY • Rhythmic movements are very primitive and automatic • Interactions and interpersonal coordinations are often based on rhythmic movements Rhythmic movements and discret movements • Rhythmic movements : • Locomotion(walking, running) • Cleaning, brushing • Sports (basket) Rhythmic movements Discret movements Upper limbs « non regular » « regular » Lower limbs « regular » « non regular » 5
  6. 6. MAIN PROPERTIES IN INTERPERSONAL INTERACTION AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Involuntary interpersonal coordination Compromise between engine control and interaction 2 1 An interaction is always based on two main aspects: • The individual part of each participant • The share of the coupling in the pair of individuals Phenomenon of emergence of coordination is so powerful that humans can not avoid involuntary dyadic motor coordination interpersonal coordination is based on synchronization of movements 6 Walk Walk applause
  7. 7. WHAT IS SYNCHRONIZATION OF MOVEMENTS? Interpersonal and Interlimb Coordination AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 7 extracted from [Richardson et al, 2007] Two spontaneously stable coordination patterns appear: • in-phase (0°) • anti-phase (180°) extracted from [Dumas et al, 2014] Interpersonal synchronization acts like a dynamical system based on 2 coupled non-linear oscillators
  8. 8. Mechanical system SYNCHRONIZATION OF DYNAMIC SYSTEMS 8AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Simulated biological system Ijspeert 2007 (Science) Bio-inspired robots
  9. 9. EXAMPLE OF INTERPERSONAL SYNCHRONIZATION IN PHYSICAL INTERACTION : THE HANDSHAKING Phase 1 – SoH Start of Handshake Phase 2 – PhC Physical Contact Phase 3 – MS Mutual Synchrony Phase 4 – EoH End od Handshake AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 9 Average of 5 handshakes. Subject I Subject II • Synchronization exists in interpersonal rhythmic physical interactions • Handshaking acts as coupled nonlinear oscillators [ Melnyk and Henaff, 2014, 2019] [ Tagnes and Henaff, 2016]
  10. 10. [Zehr, 2006] Rhythmic or discrete movements? [McCrea & Rybak, 2008] HUMAN RHYTHMIC MOVEMENTS ARE CONTROLLED BY CENTRAL PATTERN GENERATORS (CPG) AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 10 CPG for lower limbs (locomotion) CPG for upper limbs CPG for upper limbs ? Bilateral left-right intercations [Rybak et al. 2015] ?
  11. 11. COMPUTATIONAL MODEL OF CPG ? AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 11
  12. 12. Rhythmic level Formation level motor level COMPUTATIONAL MODEL OF CPG ? AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY CPG for lower limbsCPG for upper limbs Computational CPG for one joint 12 [Nassour, Hénaff 2014] • Model of oscillating neuron? • Model of sensory feedback?
  13. 13. MODEL OF RHYTHMIC NEURON : 13AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Neuron model = Non-linear oscillator (Rowat & Selverston model, 1993) σf σs Marder et al. (2001) intrinsic properties of biological neurons
  14. 14. Input signal CPG MODEL FOR ONE JOINT  Rhythmic generator cells : Rowat-Selverston model (Van der Pol formalism):  Interneurons :  Hebbian Plasticity :  Frequency learning based on Righetti’s rule:  Amplitude learning:  Input gain learning: AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 14 [Jouaiti, M., Caron, L., and Henaff, P. (2018).
  15. 15. LEARNING MOTOR COORDINATION AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 15
  16. 16. WAVING BACK EXAMPLE • Human waves at robot • Optical flow of the hand detected • Robot waves back and adapts to the human frequency AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 16 COUCOU ! HELLO!!!
  17. 17. WAVING BACK : CONTROL ARCHITECTURE  2 joints controlled :  CPG1 input (shoulder) : optical flow  CPG2 input (elbow): output of CPG1  CPG output: joint angular position  interest of plasticity:  faster synchronization AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 17 [Jouaiti and Hénaff, 2018b] shoulder elbow
  18. 18. WAVING BACK EXPERIMENT AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 18 Synchronization between joints and optical flow Hebbian plasticity
  19. 19. AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 19 WAVING BACK EXPERIMENT plasticity influences learning speed with amplitude and synaptic weight learning. without amplitude and synaptic weight learning. Phase portrait of rhythmic cells outputs shoulder, shoulder, elbow elbow Synchronization Measures (10 Wavings) Phase lock value
  20. 20. COORDINATED COMPLEX MOVEMENTS AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 20 shoulder elbow shoulder Circles at different speeds “infinite” sign
  21. 21. Can CPGs achieve both rhythmic and discrete movements ? 21AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY
  22. 22. OSCILLATING NEURON CAN BEHAVE AS A PID CONTROLLER 22AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Non oscillating :
  23. 23. EXPERIMENT 23 AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Joint phase portrait CPG phase portrait  Robotic arm coupled to a camera detects a target and raises towards it  when the target is reached, the arm oscillates at its own intrinsic frequency
  24. 24. RESULTS CPG can transition smoothly between both states by changing a single parameter 24 AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Phase portrait of the output of the CPG (extensor and flexor) and of the joint position Joint phase portraitCPG ouputs phase portrait
  25. 25. CONCLUSION & FUTURE WORKS  versatility and reliability of CPG controller that can adapt to varying frequencies  dynamic control without any dynamic model of the robot  can achieve both discrete and rhythmic movements  can also act as position or velocity controller  emergence of synchronisation (motor coordination) intrapersonnal and interpersonnal  Approach can be used in human/robot physical cooperation for several different rhythmic tasks in dangerous environment: Cleaning, Scrubbing, Brushing AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY 25 Dynamic control based on CPGs is very efficient for robot rhythmic motions : • for physical interactions with human and environment • but without need of accurate trajectories
  26. 26. AVRIL 19 - IMT - P. HÉNAFF - MINES NANCY Thanks for your attention… 26

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