Bayesian perception of touch for control of robot emotion

In this paper, we present a Bayesian approach for perception of touch and control of robot emotion. Touch is an important sensing modality for the development of social robots, and it is used in this work as stimulus through a human-robot interaction. A Bayesian framework is proposed for perception of various types of touch. This method together with a sequential analysis approach allow the robot to accumulate evidence from the interaction with humans to achieve accurate touch perception for adaptable control of robot emotions. Facial expressions are used to represent the emotions of the iCub humanoid. Emotions in the robotic platform, based on facial expressions, are handled by a control architecture that works with the output from the touch perception process. We validate the accuracy of our system with simulated and real robot touch experiments. Results from this work show that our method is suitable and accurate for perception of touch to control robot emotions, which is essential for the development of sociable robots

Synchronization of the Frenet-Serret linear system with a chaotic nonlinear system by feedback of states

A synchronization procedure of the generalized type in the sense of Rulkov et al [Phys. Rev. E 51, 980 (1995)] is used to impose a nonlinear Malasoma chaotic motion on the Frenet-Serret system of vectors in the differential geometry of space curves. This could have applications to the mesoscopic motion of biological filamentsComment: 12 pages, 7 figures, accepted at Int. J. Theor. Phy

A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors

A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots

The assessment of viscoelastic models for nonlinear soft materials

The increasing use of soft materials in robotics applications requires the development of mathematical models to describe their viscoelastic and nonlinear properties. The traditional linear viscoelastic models are unable to describe nonlinear strain-dependent behaviors. This limitation has been addressed by implementing a piecewise linearization (PL) in the simplest viscoelastic model, the Standard Linear Solid (SLS). In this work, we aim to implement the PL in a more complex model, the Wiechert model and compare the stress response of both linearized models. Therefore, the experimental data from the stress relaxation and tensile strength tests of six rubber-based materials is used to approximate the spring and dashpot constants of the SLS and the Wiechert model. Prior to implement the PL into the stress-strain curve of each material, the stress response from the Maxwell branches must be subtracted from this curve. By using the parameters obtained from fitting the Wiechert model into the stress relaxation curve, the response of both linearized models was improved. Due to the selection of constitutive equations evaluated, the linearized SLS model described the stress-strain curve more accurately. Finally, this work describes in details every step of the fitting process and highlights the benefits of using linearization methods to improve known models as an alternative of using highly complex models to describe the mechanical properties of soft materials

Forage Yield in Two Tropical Grasses at Different Cutting Intervals and N Levels

Cutting interval and N level determine forage yield in grasses (Whitehead, 1995). Coastcross-1 (Cynodon dactylon and Tifton 68 (Cynodon spp) are tropical grasses of high forage yield potential (Burton, 1972; Burton et al., 1993)

Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach

Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for the parameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher trade-off between accuracy and model simplicity. For that reason, this paper shows that an E-ELM that is based on Particle Swarm Optimisation (PSO) and Extreme Learning machine (ELM) can be extended to the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) with a Karnik-Mendel type-reduction layer. To evaluate the efficiency of E-ELM, the IT2-RBFNN is used as an Interval Type-2 Fuzzy Logic System (IT2 FLS) for the modelling of two popular benchmark data sets and for the prediction of chaotic time series. According to our results, E-ELM applied to the IT2-RBFNN not only outperforms adaptive-gradient-based algorithms and provides a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to pure fuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency

Recognition of walking activity and prediction of gait periods with a CNN and first-order MC strategy

In this paper, a strategy for recognition of human walking activities and prediction of gait periods using wearable sensors is presented. First, a Convolutional Neural Network (CNN) is developed for the recognition of three walking activities (level-ground walking, ramp ascent and descent) and recognition of gait periods. Second, a first-order Markov Chain (MC) is employed for the prediction of gait periods, based on the observation of decisions made by the CNN for each walking activity. The validation of the proposed methods is performed using data from three inertial measurement units (IMU) attached to the lower limbs of participants. The results show that the CNN, together with the first-order MC, achieves mean accuracies of 100% and 98.32% for recognition of walking activities and gait periods, respectively. Prediction of gait periods are achieved with mean accuracies of 99.78%, 97.56% and 97.35% during level-ground walking, ramp ascent and descent, respectively. Overall, the benefits of our work for accurate recognition and prediction of walking activity and gait periods, make it a suitable high-level method for the development of intelligent assistive robots