A neural integrator model for planning and value-based decision making of a robotics assistant

Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning

Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way humans and machines interact can help democratize robotics. With machine and deep learning techniques, robots will more easily adapt to new tasks, conditions, and environments. In this paper, we develop, implement, and evaluate the performance of the machine-learning-based HRI model in a collaborative environment. Specifically, we examine five supervised machine learning models viz. the ensemble of bagging trees (EBT) model, the k-nearest neighbor (kNN) model, the logistic regression kernel (LRK), the fine decision trees (FDT), and the subspace discriminator (SDC). The proposed models have been evaluated on an ample and modern contact detection dataset (CDD 2021). CDD 2021 is gathered from a real-world robot arm, Franka Emika Panda, when it was executing repetitive asymmetric movements. Typical performance assessment factors are applied to assess the model effectiveness in terms of detection accuracy, sensitivity, specificity, speed, and error ratios. Our experiential evaluation shows that the ensemble technique provides higher performance with a lower error ratio compared with other developed supervised models. Therefore, this paper proposes an ensemble-based bagged trees (EBT) detection model for classifying physical human–robot contact into three asymmetric types of contacts, including noncontact, incidental, and intentional. Our experimental results exhibit outstanding contact detection performance metrics scoring 97.1%, 96.9%, and 97.1% for detection accuracy, precision, and sensitivity, respectively. Besides, a low prediction overhead has been observed for the contact detection model, requiring a 102 µS to provide the correct detection state. Hence, the developed scheme can be efficiently adopted through the application requiring physical human–robot contact to give fast accurate detection to the contacts between the human arm and the robot arm.

Sequential method for fast neural population activity reconstruction in the cortex from incomplete noisy measurements

During recent years there has been a growing interest in stochastic dynamic neural fields employed for modeling and predictions in biomedical and technical systems. In this paper, given some incomplete noisy data available from sensors, we propose and explore a state estimation method for fast restorations of membrane potential in the cortex based on such measurements and the Amari equation used for simulations of neural population activity in a stochastic setting. Our novel technique relies upon a Galerkin-type spectral approximation utilized within the conventional state-space approach. Translating a stochastic system into its state-space form creates a straightforward and fruitful way to the data-driven parameter estimation, filtering, prediction and smoothing. The present study is particularly focused on establishing a nonlinear stochastic Galerkin-spectral-approximation-induced system of large size, which is further estimated by the traditional extended Kalman filter (EKF). The efficiency of calculations is the main purpose of our research. That is why the fast filtering solution devised is based on processing the incoming data incrementally, that is, by processing measurements one at a time, rather than handling them as a unified high-dimensional vector. Such sequential filters suit well for dealing with large data sets as well as with real-time on-line computations. Also, their derivation and substantiation is of great interest in the context of neural network training because of large stochastic systems arisen there. In comparison to the batch filtering, our novel algorithm reduces the computational cost of membrane potential reconstructions in terms of the amount of grid nodes N accepted in the underlying spacial discretization, significantly. Apart from its computation efficiency, this sequential method is more robust to round-off errors committed within a computer-based finite precision arithmetics than the classical EKF because of the (N × N)-matrix inversion elimination from such membrane potential calculations. The superior performance of our technique is examined and confirmed in comparison to the batch one on two known scenarios in the dynamic neural field modeling.

A dynamic neural field model of continuous input integration

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.