Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as “this is better than that” but also as “this is much better than that”), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human–robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.

This paper introduces a dataset on Human-cobot collaboration for Action Recognition in Manufacturing Assembly (HARMA3). It is a collection of RGB frames, Depth maps, RGB-to-depth-Aligned (RGB-A) frames and Skeleton data relative to actions performed by different subjects in collaboration with a cobot for building an Epicyclic Gear Train (EGT). In particular, 27 subjects executed several trials of the assembly task, which consisted of 7 actions. Data were collected in a laboratory scenario using two Microsoft®Azure Kinect cameras positioned in frontal and lateral positions. The dataset represents a good foundation for developing and testing advanced action recognition as well as action segmentation systems with far-reaching implications beyond human-cobot collaboration. Further potential applications include Computer Vision, Machine Learning, and Smart Manufacturing. Preliminary experiments for action segmentation by applying a state-of-the-art method on features extracted from RGB and skeletal data are presented in this paper, showing high-performance rates.

This study evaluates a socially interactive agent to create an embodied cobot. It tests a real-time continuous emotional modeling method and an aligned transparent behavioral model, BASSF (boredom, anxiety, self-efficacy, self-compassion, flow). The BASSF model anticipates and counteracts counterproductive emotional experiences of operators working under stress with cobots on tedious tasks. The flow experience is represented in the three-dimensional pleasure, arousal, and dominance (PAD) space. The embodied covatar (cobot and avatar) is introduced to support flow experiences through emotion regulation guidance. The study tests the model's main theoretical assumptions about flow, dominance, self-efficacy, and boredom. Twenty participants worked on a task for an hour, assembling pieces in collaboration with the covatar. After the task, participants completed questionnaires on flow, their affective experience, and self-efficacy, and they were interviewed to understand their emotions and regulation during the task. The results suggest that the dominance dimension plays a vital role in task-related settings as it predicts the participants' self-efficacy and flow. However, the relationship between flow, pleasure, and arousal requires further investigation. Qualitative interview analysis revealed that participants regulated negative emotions, like boredom, also without support, but some strategies could negatively impact well-being and productivity, which aligns with theory.

This paper introduces the control architecture of a platform aimed at promoting good mental health for workers interacting with collaborative robots (cobots). The platform aim is to render industrial production cells capable of automatically adapting their behavior in order to improve the operator’s quality of experience and level of engagement and to minimize his/her psychological strain. In order to achieve such a goal, an extremely rich and complex framework is required. Starting from the identification of the parameters that could influence the collaboration experience, the envisioned human- driven control structure is presented together with a detailed description of the components required to implement such an automated system. Future works will include proper tuning of control parameters with dedicated experimental sessions, together with the definition of organizational and technical guidelines for the design of a mental-health-friendly cobot-based manufacturing workplace.

To allow wheelchair (electronic or manual) users to practice driving in different safe, repeatable and controlled scenarios, the use of simulator as a training tool is considered here. In this context, the capabilities of providing high fidelity motions for users of the simulator is highlighted as one of the most important aspects for the effectiveness of the tool. For this purpose, the motion cueing algorithm (MCA) is studied in our work to regenerate wheelchair motion cues by transforming motions of the real or simulated wheelchair into the simulator motion. The studied algorithm is developed based on Model Predictive Control (MPC) approach to efficiently optimize the motions of the platform. The overall problem is formulated using mixed-integer quadratic programming (MIQP) which involves not only the vestibular model, strict constraints of the platform but also the perception threshold in the optimization cost function. In the end, the performance assessment of the system using different control techniques is analyzed, showing the effectiveness of the proposed approach in the simulation environment.

This paper presents LINarm, a device for at-home robotic upper-limb neurorehabilitation. Exploiting peculiar aspects of variable-stiffness actuators, it features functionalities widely addressed by devices specifically designed for assisted rehabilitation as controlled motion, force feedback and safety, together with the low-cost requirement for a widespread installation at patients' home.

This paper presents a parallel and partially decoupled mechanism characterized by three translational and two rotational degrees of freedom. A set of parallel kinematic chains actuates five degrees of freedom of the mobile platform and constrains one of its rotations. Its kinematics combines advantages typical of parallel architectures, as high dynamics, with positive aspects of partially decoupled ones, in terms of mechanical design, control and motion planning, through a relatively simple direct kinematic formulation. The presented architecture constitutes the mechanical heart of a robotic prototype designed to actively support the patient's head in open-skull awake surgery.

The hand-eye problem consists in computing the poses between pairs of different coordinate frames fixed to the same rigid body from measurements of such poses as the body moves. Various procedures have been proposed over the past two decades for solving this problem in presence of noise, especially for a robot as the moving body. As a matter of fact, different formulations of the problem in terms of the well known AX=XB or AX=ZB equations implement different flavors of an error minimization procedure, either least-square or non-linear, on the basis of a common algebra. It is shown in this paper that better results in terms of accuracy can be obtained outside the conventional approach. Rather than fitting the calibration matrices out of a number of random poses, the presented method superimposes easily programmable robot poses in order to attain a set of constant manifolds, like points, circles and axes, among the different coordinate frames. Such manifolds are used for identifying the constant relationships between the coordinate frames that are in fact the poses under estimation. The proposed method presents the implementation of a simple robot motion routine for generating the manifolds. Standard mathematical tools are used for fitting the manifolds out of an actual realization of the procedure with tracked markers. The geometry of the proposed manifolds also reduces the propagation of the measurement noise that usually affects the conventional computation based on relative poses. Results are given in simulation and with a real setup in comparison with the most popular state-of-the-art algorithms.

This work presents a robotic platform for upper-limb rehabilitation robotics. It integrates devices for human multi-sensorial feedback for engaging and immersive therapies. Its modular software design and architecture allows the implementation of advanced control algorithms for effective and customized rehabilitations. A flexible communication infrastructure allows straightforward devices integration and system expandability.

Until now, the presence of fences is a technological barrier for the adoption of robots in small medium enterprises (SME). The work deals with the definition of an intrinsically safe algorithm to avoid collisions between an industrial manipulator and obstacles in its workspace (Standard ISO 10218-1). The suggested strategy aims to offer an industrial solution to the problem: an off-line analysis of the workspace is performed to have an exhaustive and intrinsically description of the static obstacles and a safe spatial grid of ¿pass-through points¿ is calculated; an on-line algorithm, based on an enhanced artificial potential field evaluates the most suitable points to avoid collisions against obstacles and perform a realtime replanning the path of the robot. A Matlab toolbox that elaborates STL CAD files has been developed to obtain a full description of the workcell, and the avoidance algorithm has been designed and implemented in a standard industrial controller. Various experimental results are reported by using a COMAU NS16 arm manipulator.