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Jouaiti M, Caron L, Hénaff P. Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking. Front Neurorobot 2018; 12:29. [PMID: 29937725 PMCID: PMC6002514 DOI: 10.3389/fnbot.2018.00029] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 05/17/2018] [Indexed: 11/13/2022] Open
Abstract
It is well-known that human social interactions generate synchrony phenomena which are often unconscious. If the interaction between individuals is based on rhythmic movements, synchronized and coordinated movements will emerge from the social synchrony. This paper proposes a plausible model of plastic neural controllers that allows the emergence of synchronized movements in physical and rhythmical interactions. The controller is designed with central pattern generators (CPG) based on rhythmic Rowat-Selverston neurons endowed with neuronal and synaptic Hebbian plasticity. To demonstrate the interest of the proposed model, the case of handshaking is considered because it is a very common, both physically and socially, but also, a very complex act in the point of view of robotics, neuroscience and psychology. Plastic CPGs controllers are implemented in the joints of a simulated robotic arm that has to learn the frequency and amplitude of an external force applied to its effector, thus reproducing the act of handshaking with a human. Results show that the neural and synaptic Hebbian plasticity are working together leading to a natural and autonomous synchronization between the arm and the external force even if the frequency is changing during the movement. Moreover, a power consumption analysis shows that, by offering emergence of synchronized and coordinated movements, the plasticity mechanisms lead to a significant decrease in the energy spend by the robot actuators thus generating a more adaptive and natural human/robot handshake.
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Affiliation(s)
| | - Lancelot Caron
- Information and Systems Department, Ecole Nationale Supérieure des Mines de Nancy, Nancy, France
| | - Patrick Hénaff
- Université de Lorraine, CNRS, Inria LORIA, Nancy, France.,Information and Systems Department, Ecole Nationale Supérieure des Mines de Nancy, Nancy, France
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Melnyk A, Pitti A. Synergistic control of a multi-segments vertebral column robot based on tensegrity for postural balance. Adv Robot 2018. [DOI: 10.1080/01691864.2018.1483209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Artem Melnyk
- Héphaïstos Project, Université Côte d'Azur, INRIA, France
| | - Alexandre Pitti
- Laboratoire ETIS, Université Paris Seine, Université de Cergy-Pontoise, CNRS UMR, ENSEA, Cergy-Pontoise, France
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Wei X, Yu F, Dai M, Xie C, Wan G, Wang Y, Dou Z. Change in Excitability of Cortical Projection After Modified Catheter Balloon Dilatation Therapy in Brainstem Stroke Patients with Dysphagia: A Prospective Controlled Study. Dysphagia 2017; 32:645-656. [PMID: 28550485 PMCID: PMC5608794 DOI: 10.1007/s00455-017-9810-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 05/11/2017] [Indexed: 12/30/2022]
Abstract
Although the modified balloon dilatation therapy has been demonstrated to improve pharyngeal swallowing function post stroke, the underlying neural mechanisms of improvement are unknown. Our aims are (1) to investigate the effect of modified balloon dilatation on the excitability of corticobulbar projections to the submental muscle in dysphagic patients with brainstem stroke and (2) the relation between changes in excitability and pharyngeal kinematic modifications. Thirty patients with upper esophageal sphincter (UES) dysfunction due to unilateral brainstem stroke were recruited into two groups. The patients in dilatation group received modified balloon dilatation and conventional therapies, and those in control were only treated by conventional therapies (twice per day). The amplitudes of bilateral submental motor evoked potentials (MEPs) induced by transcranial magnetic stimulations over bilateral motor cortex, diameters of UES opening (UOD) and maximal displacement of hyoid (HD) were all assessed at baseline and the endpoint of treatments. Repeated ANOVA analysis revealed significant main effect of group, time and MEP laterality on MEP amplitudes (p = 0.02). There were no differences in the pretreatment measures between groups (all p > 0.05). After treatment, the amplitudes of affected submental MEP evoked by ipsilateral cortical pulse as well as UOD and HD were significantly different in dilatation group compared to control (amplitude: p = 0.02, UOD: p < 0.001, HD: p = 0.03). The differences of pre- and post-treatment amplitudes of the affected MEP evoked by ipsilateral stimulation showed a positive correlation with the improvement of HD (dilatation: R 2 = 0.51, p = 0.03; control: R 2 = 0.39, p = 0.01), rather than UOD in both groups (all p > 0.05). In conclusion, modified balloon dilatation therapy can increase the excitability of affected projection in patients with unilateral brainstem stroke.
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Affiliation(s)
- Xiaomei Wei
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
| | - Fan Yu
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
- 0000 0004 1760 4628grid.412478.cDepartment of Rehabilitation Medicine, Shanghai General Hospital, No. 100 Haining Road, Hongkou District, Shanghai, 200080 China
| | - Meng Dai
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
| | - Chunqing Xie
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
| | - Guifang Wan
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
| | - Yujue Wang
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
| | - Zulin Dou
- 0000 0001 2360 039Xgrid.12981.33Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou, 510630 Guangdong China
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Hunt A, Szczecinski N, Quinn R. Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot. Front Neurorobot 2017; 11:18. [PMID: 28420977 PMCID: PMC5378996 DOI: 10.3389/fnbot.2017.00018] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 03/15/2017] [Indexed: 11/17/2022] Open
Abstract
Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems.
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Affiliation(s)
- Alexander Hunt
- Department of Mechanical and Materials Engineering, Portland State UniversityPortland, OR, USA
| | - Nicholas Szczecinski
- Department of Mechanical and Aerospace Engineering, Case Western Reserve UniversityCleveland, OH, USA
| | - Roger Quinn
- Department of Mechanical and Aerospace Engineering, Case Western Reserve UniversityCleveland, OH, USA
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Nassour J, Hénaff P, Benouezdou F, Cheng G. Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots. BIOLOGICAL CYBERNETICS 2014; 108:291-303. [PMID: 24570353 DOI: 10.1007/s00422-014-0592-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 02/03/2014] [Indexed: 06/03/2023]
Abstract
In this paper, we present an extended mathematical model of the central pattern generator (CPG) in the spinal cord. The proposed CPG model is used as the underlying low-level controller of a humanoid robot to generate various walking patterns. Such biological mechanisms have been demonstrated to be robust in locomotion of animal. Our model is supported by two neurophysiological studies. The first study identified a neural circuitry consisting of a two-layered CPG, in which pattern formation and rhythm generation are produced at different levels. The second study focused on a specific neural model that can generate different patterns, including oscillation. This neural model was employed in the pattern generation layer of our CPG, which enables it to produce different motion patterns-rhythmic as well as non-rhythmic motions. Due to the pattern-formation layer, the CPG is able to produce behaviors related to the dominating rhythm (extension/flexion) and rhythm deletion without rhythm resetting. The proposed multi-layered multi-pattern CPG model (MLMP-CPG) has been deployed in a 3D humanoid robot (NAO) while it performs locomotion tasks. The effectiveness of our model is demonstrated in simulations and through experimental results.
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Affiliation(s)
- John Nassour
- Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), Munich, Germany,
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Yu J, Tan M, Chen J, Zhang J. A survey on CPG-inspired control models and system implementation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2014; 25:441-456. [PMID: 24807442 DOI: 10.1109/tnnls.2013.2280596] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper surveys the developments of the last 20 years in the field of central pattern generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging robotics-related applications. Functioning as a biological neural network, CPGs can be considered as a group of coupled neurons that generate rhythmic signals without sensory feedback; however, sensory feedback is needed to shape the CPG signals. The basic idea in engineering endeavors is to replicate this intrinsic, computationally efficient, distributed control mechanism for multiple articulated joints, or multi-DOF control cases. In terms of various abstraction levels, existing CPG control models and their extensions are reviewed with a focus on the relative advantages and disadvantages of the models, including ease of design and implementation. The main issues arising from design, optimization, and implementation of the CPG-based control as well as possible alternatives are further discussed, with an attempt to shed more light on locomotion control-oriented theories and applications. The design challenges and trends associated with the further advancement of this area are also summarized.
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Li C, Lowe R, Ziemke T. Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture. Front Neurorobot 2013; 7:5. [PMID: 23675345 PMCID: PMC3619089 DOI: 10.3389/fnbot.2013.00005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 03/06/2013] [Indexed: 11/13/2022] Open
Abstract
The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.
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Affiliation(s)
- Cai Li
- Interaction Lab, University of Skövde Skövde, Sweden
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Manoonpong P, Parlitz U, Wörgötter F. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines. Front Neural Circuits 2013; 7:12. [PMID: 23408775 PMCID: PMC3570936 DOI: 10.3389/fncir.2013.00012] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 01/21/2013] [Indexed: 11/15/2022] Open
Abstract
Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
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Affiliation(s)
- Poramate Manoonpong
- Bernstein Center for Computational Neuroscience, The Third Institute of Physics, Georg-August-Universität GöttingenGöttingen, Germany
| | - Ulrich Parlitz
- Max Planck Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-OrganizationGöttingen, Germany
- Institute for Nonlinear Dynamics, Georg-August-Universität GöttingenGöttingen, Germany
| | - Florentin Wörgötter
- Bernstein Center for Computational Neuroscience, The Third Institute of Physics, Georg-August-Universität GöttingenGöttingen, Germany
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