We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete and reliable historical trajectory data plays a pivotal role in ensuring accurate motion prediction. Therefore, we propose a new paradigm that prioritizes the reconstruction of intact historical trajectories before feeding them into the prediction modules. Our approach introduces a novel scene tokenization module to enhance the extraction and fusion of spatial and temporal features. Following this, our proposed recovery module reconstructs agents' incomplete historical trajectories by leveraging local map topology and interactions with nearby agents. The reconstructed, clean historical data is then integrated into the downstream prediction modules. Our framework is able to effectively handle missing data of varying lengths and remains robust against observation noise while maintaining high prediction accuracy. Furthermore, our recovery module is compatible with existing prediction models, ensuring seamless integration. Extensive experiments validate the effectiveness of our approach, and deployment in real-world autonomous vehicles confirms its practical utility. In the 2024 Waymo Motion Prediction Competition, our method, RMP-YOLO, achieves state-of-the-art performance, securing third place. Our code is open-source at https://github.com/ggosjw/RMP-YOLO.

Finite-time control is concerned with steering a system state to the origin before a certain settling-time limit, ignoring any consideration of when each state element converges relative to the others. In this article, a control problem called time-synchronized control is investigated, where all the system state elements have to converge to the origin at the same time. To facilitate this problem formulation, we introduce the notion of time-synchronized stability together with sufficient Lyapunov conditions. Based on these, the analytical solution of a time-synchronized stable system is obtained and discussed, explicitly offering a quantitative method to preview and predesign the control system performance in prior. Following these results, a robust time-synchronized control law is designed for multivariable systems under external disturbances and model uncertainties. Finally, comparative numerical simulations between time-synchronized control and finite/fixed/prescribed-time control are conducted to showcase the time-synchronized features attained.

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), whereat the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also anorm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.

This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no need to switch between stepping and rolling modes. To the best of our knowledge, the proposed odometry is the first work in this area. It is calculated based on the robot kinematics and instantaneous contact points of wheels with sensor inputs from IMU, joint encoders, joint torque sensors estimating wheel contact status, as well as RGB-D camera detecting geometric features of the terrain (e.g. elevation and surface normal vector). Subsequently, the odometry output is utilized as the motion model of a 3D Lidar map-based Monte Carlo Localization module for drift-free state estimation. As part of the framework, visual localization is integrated to provide high precision guidance for the robot movement relative to an object of interest. The proposed approach was verified thoroughly by two experiments conducted on the Pholus robot with OptiTrack measurements as ground truth.

This paper proposes an improved supervised autonomy framework for remote teleoperation of a quadrupedal bimanual mobile manipulator in an unknown environment, with the usage of advanced perception technology and allowing the operator to easily assist the robot with decision making for executing tasks on the fly. First, the perception system uses lightweight deep neural network-based Single Shot Detector (SSD) MobileNet on RGB images to detect objects and highlight them to the human operator via an intuitive interactive visualization interface. After object and action selections are made by the operator, segmentation of object point cloud and 3D surfaces based on random sample consensus is performed, followed by object pose localization by using keypoint extraction. Based on the localized object, mobile manipulation motion to perform the operator-selected action is planned and executed with the help of a state estimator for the hybrid wheel-legged robot. Thanks to the autonomy of the robot in perception and manipulation, the complexity of teleoperating the robot is reduced to specifying the essential task objectives. Experimental results on the real robot, with full system integration, for 2 task scenarios, namely passage clearing and object retrieval, demonstrate a high average success rate of 92.2% over a total of 90 trials.

The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample-efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores. From the highest-performing cluster, a global task representative of the cluster is identified for learning a global policy that transfers to subsequently formed new clusters, while remaining tasks in the cluster are learnt as local policies. The efficacy and efficiency of our GloCAL algorithm are compared with other approaches in the domain of grasp learning for 49 objects with varied object complexity and grasp difficulty from the EGAD! dataset. The results show that GloCAL is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1.5× longer training time.

The applicability of deep reinforcement learning algorithms to the domain of robotics is limited by the issue of sample inefficiency. As in most machine learning methods, more samples generally mean better learning effectiveness. Sample collection for robotics application is a time-consuming process in addition to safety issues for both the robot itself and the environment surrounding it that come into play for real-world scenarios. Because of these limitations, sample efficiency plays a very vital role in the field of robotic learning. To deal with this, curriculum learning offers a methodology that allows robots to suffer less from the sample collection burden required, trying to keep it at a minimum. This study aims to tackle the sample inefficiency that deep reinforcement learning algorithms face in the domain of robotics by designing a curriculum. We propose an algorithm which decides on the sequence of tasks that the agent must learn to enable the transfer of knowledge in a sample-efficient manner towards the target task. Our algorithm performs a parameter-space task representation for the purpose of deciding on the difficultiness of the tasks. Once the difficulty level of each is determined, easy tasks are learned first before the final target task. We perform a study on a double inverted pendulum setup. Simulation results showed that transfer of knowledge via curriculum is more sample efficient than a direct transfer.

We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model could be directly used for real-world visual servoing tasks. KOVIS consists of two networks. The first keypoint network learns the keypoint representation from the image using with an autoencoder. Then the visual servoing network learns the motion based on keypoints extracted from the camera image. The two networks are trained end-to-end in the simulated environment by self-supervised learning without manual data labeling. After training with data augmentation, domain randomization, and adversarial examples, we are able to achieve zero-shot sim-to-real transfer to real-world robotic manipulation tasks. We demonstrate the effectiveness of the proposed method in both simulated environment and real-world experiment with different robotic manipulation tasks, including grasping, peg-in-hole insertion with 4mm clearance, and M13 screw insertion. The demo video is available at: http://youtube/gfBJBR2tDzA.

Human-robot shared control (SC) has largely been studied in two complementary forms, namely divisible shared control (DSC) and interactive shared control (ISC). DSC enables clean division of the human and the robot subtasks, thus enabling them to work independently, while ISC allows for flexible intervention to improve the collaborative performance or experience. This paper presents a unified scheme that combines both forms of SCs to attain the benefits of flexibility as well as ease of use when human and robot jointly work on a task together. Based on the idea that flexibility should be embedded in every task constraint that the robot is controlling, we connect ISC into the robot subtask providing a soft boundary between the divided orthogonal subspaces allowing human to access the robot subtask and intervene whenever necessary. We also propose a new simple yet effective Cartesian stiffness adaptation law that enables the robot to modify its endpoint stiffness in the robot's control subspace in the presence of disagreement from the human. Simulations and real robot studies for a teleoperated path-following scenario were performed to demonstrate the flexibility of the unified shared control (USC), which allows the robot to dynamically adapt its task based on the operator's intentions.

In scenarios where the environment and task are known and certain, autonomous robotic manipulation can be performed. However, when an unforeseen situation arises where prior information is unknown, timely human assistance becomes very useful in accomplishing the task. In this paper, we propose a supervisory autonomy system that allows a user to assist the robot in uncertain task scenarios by specifying, through an intuitive user interface, the discrete actions (e.g. grasping) to be performed on the selected objects. The robot has partial knowledge of the environment in that it is able to segment and localize objects. This information is shared with the user to allow easy object and action specification, after which the robot executes the action on the object autonomously while avoiding obstacles. We investigated the system using live perception of real objects with simulated robot and environment.

Humans are adept at tool use. We can intuitively and immediately improvise and use unknown objects in our environment as tools, to assist us in performing tasks. In this study, we provide similar cognition and capabilities to robots. Neuroscientific studies on tool use have suggested that human dexterity with tools is enabled by the embodiment of the tools, which in effect, allows humans to immediately transfer prior skills acquired without tools, onto tasks requiring tool use. Here, utilizing the theoretical results from our investigations on embodiment and tool use in humans over the last years, we propose a concept and algorithm to enable similar skill transfer by robots. Our algorithm enables a robot that has had no prior learning with tools, to automatically recognize an object (seen for the first time) in its environment as a potential tool for an otherwise unattainable task, and use the tool to perform the task thereafter.

Robot learning by demonstration gives robots the ability to learn tasks which they have not been programmed to do before. The paradigm allows robots to work in a greater range of real-world applications in our daily life. However, this paradigm has traditionally been applied to learn tasks from a single demonstration modality. This restricts the approach to be scaled to learn and execute a series of tasks in a real-life environment. In this paper, we propose a multi-modal learning approach using DMP+ with linear decay integrated in a dialogue system with speech and ontology for the robot to learn seamlessly through natural interaction modalities (like an apprentice) while learning or re-learning is done on the fly to allow partial updates to a learned task to reduce potential user fatigue and operational downtime in teaching. The performance of new DMP+ with linear decay system is statistically benchmarked against state-of-the-art DMP implementations. A gluing demonstration is also conducted to show how the system provides seamless learning of multiple tasks in a flexible manufacturing set-up.

This paper is concerned with divisible shared control, which decomposes the motion space into complementary subspaces and distributes the control to the human and the robot so that each can independently effect motion control in its subspace. We present a divisible shared control scheme to assist teleoperation tasks on a curved object surface, which is difficult for a human to perform without assistance. We designed and carried an experiment to investigate its effect of user performance and work load. Experimental evaluation, based on both quantitative and qualitative measures, suggests that divisible shared control improves accuracy, speed, and smoothness, while at the same time reduces cognitive load, effort, and frustration.

In the last few years, a shift from mass production to mass customisation is observed in the industry. Easily reprogrammable robots that can perform a wide variety of tasks are desired to keep up with the trend of mass customisation while saving costs and development time. Learning by Demonstration (LfD) is an easy way to program the robots in an intuitive manner and provides a solution to this problem. In this work, we discuss and evaluate LAP, a three-stage LfD method that conforms to the criteria for the high-mix-low-volume (HMLV) industrial settings. The algorithm learns a trajectory in the task space after which small segments can be adapted on-the-fly by using a human-in-the-loop approach. The human operator acts as a high-level adaptation, correction and evaluation mechanism to guide the robot. This way, no sensors or complex feedback algorithms are needed to improve robot behaviour, so errors and inaccuracies induced by these subsystems are avoided. After the system performs at a satisfactory level after the adaptation, the operator will be removed from the loop. The robot will then proceed in a feed-forward fashion to optimise for speed. We demonstrate this method by simulating an industrial painting application. A KUKA LBR iiwa is taught how to draw an eight figure which is reshaped by the operator during adaptation.

This paper presents a control design for a desktop telepresence robot that guarantees satisfaction of field-of-view (FOV) constraints when dynamically tracking multiple points of interest on a person.The multi-point tracking problem is solved by complementing centroid tracking with local constraint satisfaction that is achieved by local integral barrier functions active only in small regions near the FOV limits. Such a control provides an aggregate view of the points of interest on the person and ensures that none of them goes out of view. A simulation study illustrates the performance of the proposed control in comparison with a conventional control.

This poster presents simulation of a tele-operated shared controlled robot task that is integrated with a generic simulator of RADOE (Robot Application Development and Operating Environment). A customized and extendable Rviz interface plugin is designed and applied to import models, do simulation, enable real robot operation, and communicate with other projects by clicking related buttons. In the simulation process, the robot model in the simulator is controlled and visualized by human operator using an Omega 7 haptic device and automatic method, i.e., the shared control. After the simulation is conducted and satisfied, the system will send back a signal to the real robot system to execute the operation task; otherwise, simulation of the shared control process will be continued until satisfaction. We provide a simulation of a drawing task on the surface of a sphere.

Dynamic Movement Primitives (DMPs) are a generic approach for trajectory modeling in an attractor land-scape based on differential dynamical systems. DMPs guarantee stability and convergence properties of learned trajectories, and scale well to high dimensional data. In this paper, we propose DMP+, a modified formulation of DMPs which, while preserving the desirable properties of the original, 1) achieves lower mean square error (MSE) with equal number of kernels, and 2) allows learned trajectories to be efficiently modified by updating a subset of kernels. The ability to efficiently modify learned trajectories i) improves reusability of existing primitives, and ii) reduces user fatigue during imitation learning as errors during demonstration may be corrected later without requiring another complete demonstration. In addition, DMP+ may be used with existing DMP techniques for trajectory generalization and thus complements them. We compare the performance of our proposed approach against DMPs in learning trajectories of handwritten characters, and show that DMP+ achieves lower MSE in position deviation. We demonstrate in a second experiment that DMP+ can efficiently update a learned trajectory by updating only a subset of kernels. The update algorithm achieves modeling accuracy comparable to learning the adapted trajectory with the original DMPs.

In this paper, we propose a framework to analyze the interactive behaviors of humans and robots in physical interactions. Game theory is employed to describe the system under study, and policy iteration is adopted to provide a solution of Nash equilibrium. The human's control objective is estimated based on the measured interaction force, and it is used to adapt the robot's objective such that human-robot coordination can be achieved. The validity of the proposed method is verified through a rigorous proof and experimental studies.

In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains (g(·)) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control.

Learning from demonstration (LfD) provides an easy and intuitive way to program robot behaviours, potentially reducing development time and costs tremendously. This is especially appealing for manufacturers interested in using industrial manipulators for high-mix production, since this technique enables fast and flexible modifications to the robot behaviours and is thus suitable to teach the robot to perform a wide range of tasks regularly. We define a set of criteria to assess the applicability of state-of-the-art LfD frameworks in the industry. A three-stage LfD method is then proposed, which incorporates human-in-the-loop adaptation to iteratively correct a batch-learned policy to improve accuracy and precision. The system will then transit to open-loop execution of the task to enhance production speed, by removing the human teacher from the feedback loop. The proposed LfD framework addresses all criteria set in this work.

In this paper, a role adaptation method is developed for human-robot collaboration based on game theory. This role adaptation is engaged whenever the interaction force changes, causing the proportion of control sharing between human and robot to vary. In one boundary condition, the robot takes full control of the system when there is no human intervention. In the other boundary condition, it becomes a follower when the human exhibits strong intention to lead the task. Experimental results show that the proposed method yields better overall performance than fixed-role interactions.

In this paper, we propose an adaptive optimal control for a robot to collaborate with a human. Game theory and policy iteration are employed to analyze the interactive behaviors of the human and the robot in physical interactions. The human's control objective is estimated and it is used to adapt the robot's own objective, such that human-robot coordination can be achieved. An optimal control is developed to guarantee that the robot's control objective is realized. The validity of the proposed method is verified through rigorous analysis and experiment studies.

In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human's intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared with fixed-role interactions.

This paper presents the detection and localization methods of entrance and staircase markers for the team E-Mobile in TechX Challenge 2013. Autonomous vehicles are required to detect and locate traffic cones beside the indoor entrance and staircase. One big challenge is from the unpredictable lighting conditions and environment. Different practical techniques such as color space selection, segmentation, shape analysis, distance estimation, and detector training are combined to obtain good detection rate and localization accuracy. The proposed methods can achieve satisfactory performance in real-world experiments.

The application of robotics to telepresence can enhance user interaction experience by providing embodiment, engaging behaviors, automatic control, and human perception. This paper presents a new telepresence robot with gesture-based attention direction to orient the robot towards attention targets according to human deictic gestures. Gesture-based attention direction is realized by combining Localist Attractor Network (LAN) and Short-Term Memory (STM).We also propose audio-visual fusion based on context-dependent prioritization among the 3 types of audio-visual cues (gesture, speech source location, head location). Experiment results are very promising and show that i) the average gesture recognition rate is 92%, i) gesture-based attention direction rate is 90%, and that ii) only by considering the 3 types of audio-visual cues together can the robot perform on par with a human in directing attention to the correct person in a meeting scenario.

Teleoperated grasping requires the abilities to follow the intended trajectory from the user and autonomously search for a suitable pre-grasp pose relative to the object of interest. Challenges include dealing with uncertainty due to the noise of teleoperator, human elements and calibration errors in the sensors. To address these challenges, an effective and robust algorithm is introduced to assist grasping during teleoperation. Although without premature object contact or regrasping strategies, the algorithm enable the robot to perform online adjustments to reach a pre-grasp pose for a final grasping. We use three infra-red (IR) sensors that are mounted on the robot hand, and design an algorithm that controls the robot hand to grasp objects using the information from the sensors readings and the interface component. Finally, a series of experiments demonstrate that the system is robust when grasping a wide range of objects and even tracks mobile objects. Empirical data from a 5-subject user study allows us to tune the relative contributions from the IR sensors and the interface component, so as to achieve a balance of grasp assistance and teleoperation.

A singularity-robust inverse kinematics framework with self-collision avoidance is proposed for a 7 degree-of-freedom (DOF) robot arm, based on minimization of energy consumption. We consider a fully revolute and redundant robot arm, consisting of two spherical joints located at the shoulder and the wrist, connected by a hinge joint at the elbow. This kinematic configuration allows the elbow to swivel freely about an axis joining the wrist and shoulder, thus allowing the redundancy to be parameterized by a single variable, namely the swivel angle. Closed form solutions for the inverse kinematics (IK) problem exist if the elbow position is known. Generally, a set of valid IK solutions, which comply with structural constraints, can be obtained from the entire range of solutions that are generated by swiveling the elbow through 360°. An objective function is proposed to determine the optimal joint trajectory based on a minimum energy criterion. To complete the framework, the issue of kinematic singularity is handled by using the concept of energy minimization.

The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement. © 2012 IEEE.

Motivated by applications in robot-assisted physical rehabilitation, this paper presents an adaptive control design for robot manipulators operating in an ellipsoidal constrained region. The ellipsoidal constraint problem is more challenging than the box constraint problem tackled in previous works, since the nonlinear constraint boundary cannot be handled in a decoupled manner along the dimensions of the task space. We introduce a novel Barrier Lyapunov Function (BLF) which contains a quotient of the squared norm of the tracking error over the ellipsoidal task space constraint. This function allows the task space constraint to be handled directly without requiring an intermediate mapping to the error space. We show that, under the proposed BLF-based adaptive control, the end-effector always remains in the constrained region despite the perturbing effects of online parameter adaptation and also the presence of bounded external disturbances. A simulation example illustrates the performance of the proposed control.

In order to provide a user-friendly system with simple operation command to grasp different objects successfully, this paper describes a combined approach of real time remote vision-based teleoperation and autonomy for a human-aided robotic grasping. In the teleoperation process, motion tracking is carried out by Kinect in real time to detect the positions of the human shoulder, elbow and hand joints such that the robot can imitate the human. Hand gestures are recognized and used to activate autonomous grasping, which can save time and generate more natural grasping poses. In our system, the robot fulfills some special tasks such as picking up objects using easy commands with Kinect as object sensor. Experiment results show that it is effective and user-friendly.

In this paper, model-free impedance control is designed for the safe human-robot interaction. A passive impedance model is imposed on the robot and a control method is proposed to guarantee the robot dynamics governed by the target model. The proposed method does not require any model information except for upper bounds of system matrix. It is thus easy to apply to practical implementation. The rigorous analysis of the control performance and robustness is presented. The validity of the proposed method is verified on the six degrees-of-freedom (DOF) PUMA 560 robot arm through simulation.

We present adaptive admittance control of a robotic manipulator, with uncertain dynamic parameters, operating in a constrained task space. To provide compliance to external forces, we generate a differentiable reference trajectory that remains in the constrained task space. Then, adaptive backstepping control, based on a time-varying asymmetric Barrier Lyapunov Function (BLF), is designed to achieve tracking of the reference trajectory while guaranteeing constraint satisfaction. The improved BLF-based control renders the entire constrained task space positively invariant. Despite transient perturbations by external forces and online parameter adaptation, practical tracking of the reference trajectory is achieved without transgression of the constrained task space. In the absence of interaction forces, asymptotic tracking of the desired trajectory is achieved.

In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc.