Henaff, Patrick

Jouaiti, Melanie

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HAL Id: hal-02024599https://hal.archives-ouvertes.fr/hal-02024599Submitted on 19 Feb 2019HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Plastic CPG-based Robot Controllers for Human-RobotRhythmic InteractionsMelanie Jouaiti, Patrick HenaffTo cite this version:Melanie Jouaiti, Patrick Henaff. Plastic CPG-based Robot Controllers for Human-Robot RhythmicInteractions. JNRH 2018 - Journées Nationales de la Robotique Humanoïde, Jun 2018, Nancy, France.￿hal-02024599￿Plastic CPG-based Robot Controllers for Human-Robot RhythmicInteractionsMelanie Jouaiti1 and Patrick Henaff2I. INTRODUCTIONIn the last few years, social robotics has been widelydeveloped with the problematic of making robots moreacceptable. An aspect which should not be neglected is thesocial adequacy and especially the synchronization phenom-ena which tend to emerge consciously, or unconsciouslywhen humans interact with each other ([1]). Humans requiretheir interaction partner to behave in an appropriate way inorder to connect, that is, a socially acceptable response inaccord with the social and human context is expected.Waving is an important part of the gestural communica-tion, creating a synchronous state where both partners wavesimultaneously. The rhythmic nature of this gesture leadsus to believe that it induces the same synchronization andlocking phenomena as other rhythmic movements.Intrinsically rhythmic bio-inspired robot controllers, suchas Central Pattern Generators (CPGs), which also incorpo-rate synchronization learning abilities, are able to producerhythmic movements and trigger the emergence of a syn-chronization in the interaction.In this work, plastic CPGs are implemented, i.e. CPGsendowed with neural and synaptic Hebbian plasticity, in thejoints of the Pepper robot in order to achieve synchronizationwith a human partner waving with a changing frequency.II. THE ROBOT CONTROLLERFor the CPG we use the half-center model proposed by [2].The CPG architecture and principle are represented on Figure1. For the rhythm generator neurons, Rowat-Selverston (RS)cells are used:V̇ = y (1)ẏ =(σf −τmτs− 1− σf tanh2(σfAfV))yτm− 1 + σsτsτmV +Afτsτmtanh(σfVAf) (2)with V the membrane potential and τm and τs timeconstants, Af influences the output amplitude, while σfdetermines whether the neuron is able to oscillate or not.σs is a gain. For more details, refer to [3].Since the RS model is a generalized Van der Pol oscillator,Hebbian mechanisms designed to learn the frequency, input*This work was not supported by any organization1Melanie Jouaiti is with Université de Lorraine, CNRS, Inria, LORIA,F-54000 Nancy, France melanie.jouaiti@loria.fr2Patrick Henaff with Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France patrick.henaff@loria.frFig. 1. CPG architecture employed. The input of the first CPG is theoptical flow detected, while the input of the second CPG is the output ofthe first.and output amplitudes and inspired by ([4]) can be integrated,enabling it to synchronize with an external signal:σ̇si{E,F} =2εFi√τmτs(1 + σsi{E,F} − σf )yi{E,F}√V 2i{E,F} + y2i{E,F}(3)Ȧfi{E,F} = −µ(νσfVi{E,F}Afi{E,F})2− Fi2 (4)ε̇i{E,F} = λtanh2(ξF )(1− (εi{E,F}Fi)2)(5)with µ and λ, learning steps and ν and ξ, scaling factors.III. RESULTSThe experiment can be divided into four observed mainphases (see Figure 2, the different phases are delimited).Learning phase (from t = 1.7 s to t = 7 s): The humanstarts waving slowly and the σs instantly decrease.Slow waving - permanent phase (from t = 7.5 s to t =21 s): The human partner is still waving slowly, σs is stablearound 12 which corresponds to an intrinsic frequency of 0.5Hz for the CPG. Both CPG outputs are also in phase withthe optical flow detected.Fast waving (from t = 21 s to t = 41 s): The humanpartner accelerates the waving. The σs adapt and startincreasing. The new stability value is around 40 (intrinsicfrequency of 1 Hz for the CPG).No waving (from t = 41 s to t = 50 s): The waving hasceased. Both σS remain stable, showing that the CPG retainsthe new frequency learned.2060100out$tσ S−50050optical flowShoulder CPG0.200.350.500 10 20 30 40 50−50050optical flowtime (s)Elbow CPG−1.6−1.2−0.8Fig. 2. Above, evolution of σS : in red and blue for the shoulder CPG;in green and purple for the elbow CPG. Note that the extensor and flexorcurves are merged for both CPGs. Below, in red, the optical flow detected. Inblue, the position command sent to the shoulder joint. In green, the positioncommand sent to the elbow joint.A. Evaluation of CoordinationThe Phase Locking Value (PLV) has been introduced by[5] to measure coordination in brain signals.Figure 3 represents the superimposed PLV for the teninteractions. It can be plainly observed that though theshoulder joint quickly coordinates with the optical flow (t= 7 s), the elbow joint experiences a longer transition phasebefore achieving coordination (t = 9.7 s). Similarly, at t =20 s, when the waving frequency changes, the transition ismore distinct for the elbow joint than for the shoulder joint.Waving in the higher frequency appears less stable and withmore variability than for the lower frequency, especially forthe elbow joint.IV. CONCLUSIONCPG-based controllers endowed with plasticity mecha-nisms are able to synchronize with an external visual signal.This synchronization property enables motor synchronization0.00.40.8out$tShoulder PLV0 10 20 30 40 500.00.40.8out$tElbow PLVtime (s)Figure 3. Top, PLV for the shoulder joint and the opticalflow; Bottom, PLV for the elbow joint and the optical flow.The PLV for ten interactions are superimposed.which in turn, triggers the emergence of social synchroniza-tion between the robot and the human. This was achieved byanalyzing the synchronization between the optical flow andarticular command values across ten different interactionsthanks to the PLV. Despite the obvious human variability inthe different interactions, results show an interesting coher-ence with signals often overlapping. Thus, the coordinationobserved previously is not merely due to chance but thecontroller indeed triggers the emergence of coordination (thePLV tends to reach 1.0) after a transitory phase where thesystem adapts to the new input information.REFERENCES[1] E. Delaherche, M. Chetouani, A. Mahdhaoui, C. Saint-Georges, S. Vi-aux, and D. Cohen, “Interpersonal synchrony: A survey of evaluationmethods across disciplines,” IEEE Transactions on Affective Computing,vol. 3, no. 3, pp. 349–365, 2012.[2] I. A. Rybak, N. A. Shevtsova, M. Lafreniere-Roula, and D. A. McCrea,“Modelling spinal circuitry involved in locomotor pattern generation:insights from deletions during fictive locomotion,” The Journal ofphysiology, vol. 577, no. 2, pp. 617–639, 2006.[3] P. F. Rowat and A. I. Selverston, “Modeling the gastric mill centralpattern generator of the lobster with a relaxation-oscillator network,”Journal of neurophysiology, vol. 70, no. 3, pp. 1030–1053, 1993.[4] L. Righetti, J. Buchli, and A. J. Ijspeert, “Dynamic hebbian learningin adaptive frequency oscillators,” Physica D: Nonlinear Phenomena,vol. 216, no. 2, pp. 269–281, 2006.[5] J.-P. Lachaux, E. Rodriguez, J. Martinerie, F. J. Varela, et al., “Measur-ing phase synchrony in brain signals,” Human brain mapping, vol. 8,no. 4, pp. 194–208, 1999.

Plastic CPG-based Robot Controllers for Human-Robot Rhythmic Interactions

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Plastic CPG-based Robot Controllers for Human-Robot Rhythmic Interactions

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