Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot

Debnath, Shoubhik; Nassour, John; Cheng, Gordon

doi:10.1109/humanoids.2014.7041489

Cited by 9 publications

(5 citation statements)

References 13 publications

(10 reference statements)

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“…Inter-neurons of pattern formation layer (neuron PF), sensory neurons (neuron SN) for afferent feedbacks and motoneurons (neurons MN) for efferent signals, are defined as a sigmoid function (Debnath et al, 2014 ; Nassour et al, 2014 ):…”

Section: Methodsmentioning

confidence: 99%

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

2018

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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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Section: Methodsmentioning

confidence: 99%

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

2018

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“…It can also takes sensory feedback into account. While this model is widely used for locomotion [20], [21], [22], very few works apply it to arm movements: to our knowledge, only [23] used it to study the reaching movement.…”

Section: Related Workmentioning

confidence: 99%

Motor Coordination Learning for Rhythmic Movements

Jouaiti

Hénaff

2019

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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The perspective of ubiquitous robots raises the issue of social acceptance. It is our belief that a successful robot integration relies on adequate social responses. Human social interactions heavily rely on synchrony which leads humans to connect emotionally. It is henceforth, our opinion, that motor coordination mechanisms should be fully integrated to robot controllers, allowing coordination, and thus social synchrony, when required. The aim of the work presented in this paper is to learn motor coordination with a human partner performing rhythmic movements. For that purpose, plastic Central Pattern Generators (CPG) are implemented in the joints of the Pepper robot. Hence, in this paper, we present an adaptive versatile model which can be used for any rhythmic movement and combination of joints. This is demonstrated with various arm movements.

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“…To produce movements, the central pattern generator model (MLMP-CPG) proposed by Nassour et al ( 2014 ) has been used in Debnath et al ( 2014 ). This CPG model has three layers: rhythm-generation layer (RG), pattern-formation layer (PF), and motorneuron layer (MN), see Figure 1 .…”

Section: Movement Generationmentioning

confidence: 99%

Learning of Central Pattern Generator Coordination in Robot Drawing

2018

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How do robots learn to perform motor tasks in a specific condition and apply what they have learned in a new condition? This paper proposes a framework for motor coordination acquisition of a robot drawing straight lines within a part of the workspace. Then, it addresses transferring the acquired coordination into another area of the workspace while performing the same task. Motor patterns are generated by a Central Pattern Generator (CPG) model. The motor coordination for a given task is acquired by using a multi-objective optimization method that adjusts the CPGs' parameters involved in the coordination. To transfer the acquired motor coordination to the whole workspace we employed (1) a Self-Organizing Map that represents the end-effector coordination in the Cartesian space, and (2) an estimation method based on Inverse Distance Weighting that estimates the motor program parameters for each SOM neuron. After learning, the robot generalizes the acquired motor program along the SOM network. It is able therefore to draw lines from any point in the 2D workspace and with different orientations. Aside from the obvious distinctiveness of the proposed framework from those based on inverse kinematics typically leading to a point-to-point drawing, our approach also permits of transferring the motor program throughout the workspace.

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Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot

Cited by 9 publications

References 13 publications

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

Motor Coordination Learning for Rhythmic Movements

Learning of Central Pattern Generator Coordination in Robot Drawing

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