Patrick Henaff

Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots

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.

On the Role of Sensory Feedbacks in Rowat–Selverston CPG to Improve Robot Legged Locomotion

This paper presents the use of Rowat and Selverston-type of central pattern generator (CPG) to control locomotion. It focuses on the role of afferent exteroceptive and proprioceptive signals in the dynamic phase synchronization in CPG legged robots. The sensori-motor neural network architecture is evaluated to control a two-joint planar robot leg that slips on a rail. Then, the closed loop between the CPG and the mechanical system allows to study the modulation of rhythmic patterns and the effect of the sensing loop via sensory neurons during the locomotion task. Firstly simulations show that the proposed architecture easily allows to modulate rhythmic patterns of the leg, and therefore the velocity of the robot. Secondly, simulations show that sensori-feedbacks from foot/ground contact of the leg make the hip velocity smoother and larger. The results show that the Rowat–Selverston-type CPG with sensory feedbacks is an effective choice for building adaptive neural CPGs for legged robots.

Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

This paper describes experimental results regarding the real time implementation of continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation through time (BPTT) algorithm for the on-line learning control laws. Experiments are carried out to control the balance of a biped robot prototype in its standing posture. The neural controller is trained to compensate for external perturbations by controlling the torsos joint motions. Algorithms are embedded in the real time electronic unit of the robot. On-line learning implementations are presented in detail. The results on learning behavior and control performance demonstrate the strength and the efficiency of the proposed approach.

A new control strategy for ROBIAN biped robot inspired from human walking

In this paper, we show that a biped robot can walk dynamically using a simple control technique inspired from human locomotion. We introduce four critical angles that affect robot speed and step length. Our control approach consists in tuning the PID parameters of each joint for increasing stability of the walk. This method could be easily implemented in real time because it needs acceptable calculation time. We validated the control approach to a dynamic simulation of our 14DOF biped called ROBIAN. A comparison with human walking is presented and discussed. We prove that we can maintain robot stability and walk cyclepsilas repetition without referencing a predefined trajectory or detecting the center of pressure. Results show that the walk of the biped is very similar to human one. A power consumption analysis confirms that our approach could be implemented on the real robot ROBIAN.

Three DOF hybrid mechanism for humanoid robotic application: Modeling, design and realization

This paper deals with a research work aimed to develop a new three degrees of freedom (DOF) mechanism for humanoid robots. The main idea is to build hybrid (3DOF) mechanism, which avoids the drawbacks of the serial and parallel mechanisms. The new solution has to merge the advantages of both classical (serial and parallel) structures in order to achieve optimal performances. The proposed mechanism can be used as a solution for several modules in humanoid robot. The hip mechanism is taken as an example to illustrate the contribution of this paper. To evaluate the performances of the system, simulation of this new mechanism is carried out with Adams software. Geometrical and Kinematic models are developed and included in the simulation tool. Based on biomechanical data, analysis of the new kinematic structure is carried out. The design of the proposed solution is then described. Finally the first prototype developed for the HYDROi¿D robots hip is presented. This mechanism is a part of an International patent accepted at INPI- France.

Experience-based learning mechanism for neural controller adaptation: Application to walking biped robots

Neurobiology studies showed that the role of the anterior cingulate cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differentiate between a learning mechanism that takes risks, and one that averts risks. The tolerance to risk plays an important role in such learning mechanism. Results have shown the differences in learning capacity between risk-taking and risk avert behaviors. In this paper, we propose a learning mechanism that is able to learn from negative and positive feedback. It is composed of two phases, evaluation and decision-making phase. In the evaluation phase, we use a Kohonen Self Organizing Map technique to represent success and failure. Decision-making is based on an early warning mechanism that enables to avoid repeating past mistakes. Our approach is presented with an implementation on a simulated planar biped robot, controlled by a reflexive low-level neural controller. The learning system adapts the dynamics and range of a hip sensor neuron of the controller in order for the robot to walk on flat or sloped terrain. Results show that success and failure maps can learn better with a threshold that is more tolerant to risk. This gives rise to robustness to the controller even in the presence of slope variations.

Sensor network architecture to measure characteristics of a handshake between humans

Handshaking is an important component of social interaction between people in many cultures. Thus, for further applications in human/humanoid-robot interaction it is important to understand and measure the characteristics of a handshake during interaction between humans. In this paper, a new wearable sensor network to measure a handshake is described. It consists of a set of several sensors (accelerometers, gyroscopes and force sensors) attached to the glove, and of a microcontroller for signal acquisition and conditioning. The paper focuses on the applicability and qualitative analysis of the proposed architecture of sensors through several experiments of handshaking between two human subjects. The results show that the proposed system allows reproducible experiments to quantify handshake characteristics such as duration and strength of the grip, vigor and rhythmicity of a handshake.

Measurement and analysis of physical parameters of the handshake between two persons according to simple social contexts

In order to facilitate and improve robots social acceptance, they must be equipped with behaviors similar to those of humans. It is therefore necessary to study and model the phenomenon to be reproduce. This paper studies and analyzes the physical parameters of the handshake in order to have its characteristic features (frequency, duration, strength, synchronization, etc.) used to model this interaction. Features that would later help to develop bio-inspired adaptive controllers, which will allow humanoid robots to better interact with humans according to simple social contexts.

Analysis of synchrony of a handshake between humans

Physical and social interaction between humans and robots are important for humanoid robotics. In this article the characteristics of a handshake between humans are physically examined aiming at future experiments with a handshake between a human and a robot. A special pair of data gloves has been designed to measure quantitative characteristics of a handshake ritual such as duration, strength of the grip, and frequency of the rhythmic movements. Experiment results show that handshaking consists of four phases. After a physical contact, a mutual synchrony appears between the two persons. A statistical analysis shows that the frequency of this synchronization is around 4 Hz and average strength of the grip is 2.5 N.

Electronic hardware design of a low cost tactile sensor device for physical human-robot interactions

This paper proposes a low-cost system, based on the method of Electrical Impedance Tomography (EIT), for data acquisition from soft conductive fabric, for the purposes of designing of robots artificial skin. A simple multiplexer/ demultiplexer circuit is used for retrieving the resistance field from the pair-wised electrodes which inject the electrical current and the electrodes which measure the output voltage from the conductive fabric. A microcontroller governs the injection of current, voltage output patterns and the analog-digital conversion, from the tactile material. After explanation of the EIT method, the electronics corresponding to the data acquisition is presented and the material characteristics are analyzed. The results show that the spatial patterns of the tactile contact can be acquired and localized in real time.