Understanding how users perceive assistive robotic systems is critical for their successful adoption, particularly in rehabilitation settings where both patients and clinicians influence decision-making. While prior work has focused on technical performance and overall usability, affective responses such as trust, control, and perceived independence are often captured using coarse, single-score measures that overlook important nuances. This study analyzes focus group discussions with individuals with spinal cord injury to examine how users evaluate different aspects of assistive robot design. A hybrid aspect-based sentiment analysis approach is applied, combining lexicon-based and transformer-based methods to capture both interpretable and context-sensitive sentiment. The analysis separates sentiment across key dimensions, including independence, functionality, safety, control, cost, and data sharing. Participants expressed consistently positive views toward independence and functional support, while responses related to safety, control, and data sharing were more conditional. In particular, trust emerged as something that depends on transparency, user control, and the ability to override system behavior, rather than a fixed attitude toward the technology. These findings suggest that successful assistive robotic systems must balance autonomy with user authority and provide clear, adaptable mechanisms for control and data governance.

It is my pleasure to present the Special Issue “Adaptive and Nonlinear Control of Robotics”, which brings together nine original research contributions exploring state-of-the-art control strategies for robotic systems operating under nonlinear dynamics, uncertain parameters, reconfiguration, or complex physical constraints [...]

Wheelchair-mounted robotic arms (WMRAs) are assistive manipulators designed to increase the functional independence of individuals with limitations in the upper and lower extremities. While previous research has identified several visual, cognitive, and physical abilities that facilitate optimal operation of such a device, these have yet to be accounted for when designing the human–robot interface. This study investigates whether interface-level compensations can improve usability and support more personalized control across users with different abilities. Five interface compensations were implemented and evaluated: object highlighting, move suggestions, slowing near objects, a one-click approach, and a level indicator. A user study was conducted among individuals with spinal cord injury as well as able-bodied younger and older adults serving as comparison groups. Evaluations of task performance metrics, perceived WMRA usability, and user experience were conducted. The results indicate that younger adults achieved the highest task efficiency, followed closely by participants with spinal cord injury, with both groups reporting good-to-excellent usability, whereas older adults showed lower efficiency and rated the system as having fair usability. The one-click automated object approach feature was identified as the most useful compensation among all participant groups. Overall, participant feedback indicated that spatial visualization and spatial orientation were the most important individual differences affecting the operation of the arm.

This paper presents the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on extensive user studies of the system, several features were implemented in the interface that could reduce the complexity of the human–robot interaction while also compensating for the deficits in different human factors, such as working memory, response inhibition, processing speed, depth perception, spatial awareness, and contrast sensitivity. To effectively and safely control the robotic arm, we designed several new features, including an adaptive human–robot interaction framework. To provide the user with a less complex and safer interaction with the robot, we added new functionalities such as ‘One-click mode’, ‘Move suggestion mode’, and ‘Gripper Control Assistant’. Furthermore, to equip our assistive robotic system with an adaptive User Interface, we designed and implemented compensators such as ‘Contrast Enhancement’, ‘Object Proximity Velocity Reduction’, and ‘Orientation Indicator’. Results from a multitude of experiments show that the system is indeed robust, safe, and computationally efficient in addition to addressing the user’s highly desired capabilities.

Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells' terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis. Note to Practitioners-Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.

We present the design and implementation of an algorithm, equipped with a switched adaptive controller, for grasping unknown objects using a robot gripper. A Lyapunov-based analysis demonstrates that the switching controller is indeed asymptotically stable with both the translational and rotational slip velocities converging to the origin. Experimental results using a novel sensorized gripper prototype and objects of different sizes, shapes, and weights show that the proposed algorithm not only ensures the prevention of slippage of the grasped objects, but is also able to apply the minimal force needed to safely grasp these objects without causing excessive deformation.

In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human–robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a six-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov-based stability analysis is provided to prove both the convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human–robot interactions for a kinematically redundant robotic manipulator.

Cyber human interaction is a broad term encompassing the range of interactions that humans can have with technology. While human interaction with fixed and mobile computers is well understood, the world is on the cusp of ubiquitous and sustained interactions between humans and robots. While robotic systems are intertwined with computing and computing technologies, the word robot here describes technologies that can physically affect and in turn be affected by their environments which includes humans. This chapter delves into issues of cyber human interaction from the perspective of humans interacting with a subset of robots known as assistive robots. Assistive robots are robots designed to assist individuals with mobility or capacity limitations in completing everyday activities, commonly called instrumental activities of daily living. These range from household chores, eating or drinking to any activity with which a user may need the daily assistance of a caregiver to complete. One common type of assistive robot is the wheelchair mounted robotic arm. This device is designed to attach to a user's wheelchair to allow him or her to complete their activities independently. In short, these devices have sensors that allow them to sense and process their environment with varying levels of autonomy to perform actions that benefit and improve the well-being of people with capability limitations or disabilities. While human robot interaction is a popular research topic, not much research has been dedicated with regard to individual with limitations. In this chapter, we provide an overview of assistive robotic devices, discuss common methods of user interaction, and the need for an adaptive compensation framework to support potential users in regaining their functional capabilities.

Motivated by grasp planning applications within cluttered environments, this paper presents a novel approach to performing real-time surface segmentations of never-before-seen objects scattered across a given scene. This approach utilizes an input 2D depth map, where a first principles-based algorithm is utilized to exploit the fact that continuous surfaces are bounded by contours of high gradient. From these regions, the associated object surfaces can be isolated and further adapted for grasp planning. This paper also provides details for extracting the six-DOF pose for an isolated surface and presents the case of leveraging such a pose to execute planar grasping to achieve both force and torque closure. As a consequence of the highly parallel software implementation, the algorithm is shown to outperform prior approaches across all notable metrics and is also shown to be invariant to object rotation, scale, orientation relative to other objects, clutter, and varying degree of noise. This allows for a robust set of operations that could be applied to many areas of robotics research. The algorithm is faster than real time in the sense that it is nearly two times faster than the sensor rate of 30 fps.

This paper studies the active cell balancing problem by using model predictive control (MPC) for real time range extension. Specifically, three MPC formulations are proposed and compared: the first one being a tracking controller to force all cells to follow the same trajectory generated by a nominal cell model, the second one trying to maximize the lowest cell SOC/voltage and the last one minimizing the difference between the highest and lowest cell SOC/voltages. Both steady state and transient conditions are simulated to assess the effectiveness of the proposed controllers, and a range extension of 4% is found for dynamic driving cycle and 7% for steady state condition. Comparing to the literature, our approaches achieve similar range extension, without making the restrictive assumption that the final battery state-of-charge is known in advance, making our approaches more applicable. Real time implementability is demonstrated via throughput analysis.

This brief presents the design, analysis, and experimental implementation, an adaptive control enabled intelligent algorithm to facilitate 1-click grasping of novel objects by a robotic gripper. The use of object-geometry free modeling coupled with utilization of interaction force and slip velocity measurements allows for the design of an adaptive backstepping controller that is shown to be asymptotically stable via a Lyapunov-based analysis. Experiments with multiple objects using a prototype gripper with embedded sensing show that the proposed scheme is able to effectively immobilize novel objects within the gripper fingers. Furthermore, it is seen that the adaptation allows for close estimation of the minimum grasp force required for safe grasping, which results in minimal deformation of the grasped object.

Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback approach to address robot grasping task using force-torque tactile sensing. While visual perception is an essential part for gross reaching, constant utilization of this sensing modality can negatively affect the grasping process with overwhelming computation. In such case, human being utilizes tactile sensing to interact with objects. Inspired by, the proposed approach is presented and evaluated on a real robot to demonstrate the effectiveness of the suggested framework. Moreover, we utilize a deep learning framework called Deep Calibration in order to eliminate the effect of bias in the collected data from the robot sensors.

In this paper, we investigate the problem of grasping previously unseen objects in unstructured environments which are cluttered with multiple objects. Object geometry, reachability, and force-closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single-view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to scenes with both single and multiple objects, in both simulation and experiments. Using sequential processing in MATLAB running on a 4th-generation Intel Core Desktop, simulation results with the benchmark Object Segmentation Database show that the algorithm takes 281 ms on average to generate the 6D robot pose needed to attach with a pair of viable grasping edges that satisfy reachability and force-closure conditions. Experimental results in the Assistive Robotics Laboratory at UCF using a Kinect One sensor and a Baxter manipulator outfitted with a standard parallel gripper showcase the feasibility of the approach in grasping previously unseen objects from uncontrived multi-object settings.

In this paper, we investigate the problem of grasping novel objects in unstructured environments. Object geometry, reachability, and force closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both MATLAB simulation and experiments using a Kinect One sensor and a Baxter manipulator.

Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback approach to address robot grasping task using force-torque tactile sensing. While visual perception is an essential part for gross reaching, constant utilization of this sensing modality can negatively affect the grasping process with overwhelming computation. In such case, human being utilizes tactile sensing to interact with objects. Inspired by, the proposed approach is presented and evaluated on a real robot to demonstrate the effectiveness of the suggested framework. Moreover, we utilize a deep learning framework called Deep Calibration in order to eliminate the effect of bias in the collected data from the robot sensors.

This paper examines the performance differences across gender and age when operating a robotic manipulator arm and also seeks to determine which human factors are considered important predictors of performance for each group. To examine these differences, 93 participants were recruited and divided up into both male (46) and female (47) as well as young (46) and old (47). While men and women had nearly identical human factors, except for women exhibiting better dexterity, different navigation strategies were utilized by the genders leading men to perform the tasks quicker and with less overall moves than women. While task completion speed was affected most by working memory (WM) and spatial abilities for men, it was seen to be mostly dependent on physical abilities for women. Substantial differences were seen between the age cohorts in WM and dexterity which resulted in the younger cohort completing tasks quicker and with a higher rate of commands than the older cohort; no difference was observed in the total number of moves which provided evidence of a similar navigation strategy across the age groups. To improve task speed performance, older adults used all facets of their information processing and spatial abilities as compared to the younger group who used a narrower subset. To compensate for the aforementioned variations in important human factors, human-computer interface design considerations are suggested.

In this paper, a robust output feedback control design is developed for suppression of aeroelastic vibration of a 2-DOF nonlinear wing section system. The aeroelastic system operates in a quasi-steady aerodynamic incompressible flowfield and is actuated using a combination of a leading-edge (LE) and a trailing-edge (TE) flap. By only utilizing measurements of pitching and plunging deflections, an innovative Lyapunov-based procedure is used to design sliding mode control inputs for the LE and TE control surface deflections. The closed-loop system is shown to have semi-global asymptotic stability even in the presence of model uncertainty and unknown external gust loading. Extensive simulation results under a variety of scenarios show the effectiveness of the control strategy.

Real time applications such as robotic require real time actions based on the immediate available data. Machine learning and artificial intelligence rely on high volume of training informative data set to propose a comprehensive and useful model for later real time action. Our goal in this paper is to provide a solution for robot grasping as a real time application without the time and memory consuming pertaining phase. Grasping as one of the most important ability of human being is defined as a suitable configuration which depends on the perceived information from the object. For human being, the best results obtain when one incorporates the vision data such as the extracted edges and shape from the object into grasping task. Nevertheless, in robotics, vision will not suite for every situation. Another possibility to grasping is using the object shape information from its vicinity. Based on these Haptic information, similar to human being, one can propose different approaches to grasping which are called grasping policies. In this work, we are trying to introduce a real time policy which aims at keeping contact with the object during movement and alignment on it. First we state problem by system dynamic equation incorporated by the object constraint surface into dynamic equation. In next step, the suggested policy to accomplish the task in real time based on the available sensor information will be presented. The effectiveness of proposed approach will be evaluated by demonstration results.

In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot's end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

This paper presents the design, analysis, and experimental implementation of an adaptive control to facilitate 1-click grasping of novel objects by a robotic gripper. Motivated by a desire to obtain a reduced-order controller, a previously developed grasp model is reparameterized to design an adaptive backstepping controller. A Lyapunov-based analysis is utilized to show asymptotic convergence of the object slip velocity to the origin. Furthermore, the analysis shows that the closed-loop controller is able to estimate the minimal steady-state force required to grasp the object. Simulation and experiment results both show that the object is immobilized within the gripper without any significant deformation.