Haptic interaction plays a crucial role in enhancing realism and immersion in virtual environments, particularly in applications such as robotic rehabilitation. In these environments, therapy based on serious games that combine VR and haptic feed promise to offer engaging, affordable and reliable treatments. Most 6-DoF haptic methods have focused on precision in the kinematic fidelity of the haptic feedback. However, rehabilitation games for upper limb focus more on the forces exerted by the patient than the kinematic precision of the contacts with the virtual environment, since these exerted forces will be what powers the neuromuscular recovery of lost movement. We believe that a haptic control method with a focus on providing stable compliant and safe collisions is required to further the research into VR rehabilitation gamification. In addition, these solutions often rely on specifically tailored simulators running at very high frequencies. In contrast, commercial game engine that bring the most capabilities for gamification run at very low frequencies in comparison (≃50 Hz). In this work, we present a novel approach that integrates god object and penalty-based methods to achieve a balance between stability and computational efficiency, to enable 6-DoF haptic force generation on a collaborative robot. This is achieved through a relaxed quasi-god object simulation in the game engine and geodesic constraints in the robot’s haptic loop.

Robotic-assisted upper limb rehabilitation has gained significant attention in recent years due to its potential to enhance the recovery process for individuals with motor impairments resulting from neurological conditions and injuries. The main rehabilitation treatments rely on the repetitive execution of a movement of the upper-limb, guided by a therapist to prevent incorrect movements and to provide the necessary support. Many of the exercises performed by therapists can be modeled as a movement in SE(3) space (position and orientation). This movement itself is one-dimensional, as it can be modeled by a one-dimensional curve. To solve a similar problem, some approaches have been proposed in human-robot interaction (HRI) following virtual guides, but are either limited to specific types of curves (e.g. without orientation) or rely on linear control methods with non-intuitive parameters. To address these limitations and enable the use of these methods in physical rehabilitation, this paper extends Cartesian impedance control to splines, which we term path impedance control. It capitalizes on the intrinsic path geometry of end-effector robotic rehabilitation systems. The primary objective of this control algorithm is to emulate the sensation of maneuvering a physical object along a wire, akin to conventional exercise machines; and, in conjunction, provide an intuitive parametrization of rehabilitation exercises. We build on existing virtual guide control strategies using non-linear control and Lie Groups to generalize the control law to any one-parameter SE(3) curve.

The maintenance and inspection of the flooded shafts, specially coal ones, is an important environmental problem. There are thousands of shafts of this type in Europe with the danger of pollution, flood and collapse. This paper presents some of the main ongoing works of the EU project STAMS that develop an autonomous underwater robotic system for periodic monitoring of flooded shafts in hazardous and complex conditions. The accurate navigation is very cluttered at 1.000 m depth conditions, where minimum visibility and unexpected obstacles are some of the difficulties to overcome. We are going beyond classical navigation approaches using only few sensor information. Another innovation is the installation of Reference Points (RPs) in the shaft's walls by the robot using a special fixation mechanism. The specially designed cases of the RPs allow to house specific sensors and help in the navigation, and will be used in periodic monitoring and assessment of the mine shafts. The positioning and attachment of these RPs is another contribution of this paper.

Robotics can play a significant role in the rehabilitation of patients with spasticity by improving their early diagnosis and reducing the costs associated with care. Spasticity is a muscle control disorder characterized by an increase in muscle tone with exaggerated stretch reflexes, as one component of the upper motor neuron syndrome. Furthermore, spasticity is present in other pathologies, such as cerebral palsy, spina bifida, brain stroke among others. This video shows the ongoing research on developing a platform for the modelling and the assessment of spasticity using collaborative robots as clinical tool. Our aim is to develop methods for non-invasive biomechanical modelling of upper limbs joints using 7-DOF Rosen Kinematics [1], mixed with a non-linear state of Hills force-velocity relation [2], improved by introducing new parameters such as rigidity, viscoelasticity, extensibility and thixotropy. After a learning phase performed by the therapist, the robot replicates the trajectories required to perform the assessment. The video also describes the detailed analysis of passive movement response (force/torque and position/velocity)of the limb. These parameters will be used to determine the degree of spasticity of patients in a fast and objective manner, while simultaneously developing new clinical scales, such as a modified version of Ashworth [3].

This article presents the design, implementation and test of a joint mechanical device created to improve the safety and performance of robots interacting physically with human beings. The mechanism consists of a series of linear springs, preloaded and arranged in such a way to take advantage of the benefits of series elastic actuators and to limit the maximum applicable torque. This is a passive mechanism because it does not have a second actuator system on the same joint. However, it involves a joint system of variable stiffness given the three distinct performance phases it has, each one having a different stiffness value, elastic torque-dependent occurring in the joint.

Programming by demonstration (PbD) allows matching the kinematic movements of a robot with those of a human. The presented Continuous Goal-Directed Actions (CGDA) is able to additionally encode the effects of a demonstrated action, which are not encoded in PbD. CGDA allows generalization, recognition and execution of action effects on the environment. In addition to analyzing kinematic parameters (joint positions/velocities, etc.), CGDA focuses on changes produced on the object due to an action (spatial, color, shape, etc.). By tracking object features during action execution, we create a trajectory in an n-dimensional feature space that represents object temporal states. Discretized action repetitions provide us with a cloud of points. Action generalization is accomplished by extracting the average point of each sequential temporal interval of the point cloud. These points are interpolated using Radial Basis Functions, obtaining a generalized multidimensional object feature trajectory. Action recognition is performed by comparing the trajectory of a query sample with the generalizations. The trajectories discrepancy score is obtained by using Dynamic Time Warping (DTW). Robot joint trajectories for execution are computed in a simulator through evolutionary computation. Object features are extracted from sensors, and each evolutionary individual fitness is measured using DTW, comparing the simulated action with the generalization.

In this paper, we study how human-robot interaction can be beneficial on the Continuous Goal-Directed Actions (CGDA) framework. Specifically, a system for robot discovery of motor primitives from random human-guided movements has been developed. These guided motor primitives (GMP) are used as scaffolds to reproduce a goal-directed actions. CGDA encodes goals as the changes produced on object features (color, area, etc) due to actions. This paper focuses on using motor primitives extracted from human-guided random robot movements to execute these goal-directed actions. The human guides the robot joints in random movements, which are later divided in small segments. These segments are compared in terms of joint positions and selected to be diverse. To perform goal-directed actions, the robot must discover an adequate sequence of GMP. To discover these sequences we organize the primitives as a tree with incremental depths (where each node represents a primitive) and use a breadth-first search. In one of the experiments performed, the robot executes a task based on spatial object features. In the other experiment, the goal is to paint a wall by following a color feature trajectory.

An approach for adaptive shared control of an assistive manipulator is presented. A set of distributed collision and proximity sensors is used to aid in limiting collisions during direct control by the disabled user. Artificial neural networks adapt the use of the proximity sensors online, which limits movements in the direction of an obstacle before a collision occurs. The system learns by associating the different proximity sensors to the collision sensors where collisions are detected. This enables the user and the robot to adapt simultaneously and in real-time, with the objective of converging on a usage of the proximity sensors that increases performance for a given user, robot implementation and task-set. The system was tested in a controlled setting with a simulated 5 DOF assistive manipulator and showed promising reductions in the mean time on simplified manipulation tasks. It extends earlier work by showing that the approach can be applied to full multi-link manipulators.

This paper presents a robot imagination system that generates models of objects prior to their perception. This is achieved through a feature inference algorithm that enables computing the fusion of keywords which have never been presented to the robot together previously. In this sense, robot imagination is defined as the robot's capability of generating feature parameter values of unknown objects by generalizing characteristics from previously presented objects. The system is first trained with visual information paired with semantic object descriptions from which keywords are extracted. Each keyword creates an instance of the learnt object in an n-dimensional feature space. The core concept behind the robot imagination system presented in this paper is the use of statistically fit hyperplanes in the feature space to represent and simultaneously extend the meaning of grounded words. The inference algorithm allows to determine complete solutions in the feature space. Finally, evolutionary algorithms are used to return these numeric values to the real world, completing an inverse semantic process.

The study of Human Postural Control (HPC) system reveals the existence of an anticipative component for improving its performance. This anticipative subsystem is able to preview future consequences caused by a postural disturbance and, as well, to trigger corrective actions. This paper presents the novel high level architecture for the humanoid robot TEO based on the same principles than the human system. The core of the architecture is an anticipative system for predicting postural corrections based on the evaluation of unexpected events or surprises. Basically, these events are caused by the mismatch between sensory perceptions and the expected. The resulting surprise is used by a decision making system that decides whether a correction is necessary, and selects the adequate graded postural correction from a set of motion patterns or synergies. All the anticipative process is enabled by Neuro-Fuzzy modules that provide the system with human style reasoning behaviour.

Assistive robots are increasingly being envisioned as an aid to the elderly and disabled. However controlling a robotic system with a potentially large amount of Degrees of Freedom (DOF) in a safe and reliable way is not an easy task, even without limitations in the mobility of the upper extremities. Shared control has been proposed as a way of aiding disabled users in controlling mobility aids such as assistive wheelchairs, by using the sensors of the robotic platform to predict the user's intent and assist in navigation. Assistive manipulators, that aim to perform physical Daily Life Activities (DLA), is a more complex problem however. This calls for good experimental practices to ensure repeatability, reproducibility, and steady progress. The work presented here attempts to model the complete system for assistive manipulators, and in the context of this model define metrics and good practices for benchmarking shared control for such robots. An adaptive shared control approach for limiting collisions during teleoperation is used as a case study. Improvements in performance are shown, quantified by the trade-off between mean time and number of collisions as well as the controllability from the user's perspective.

This article deals with the design of the full-size humanoid robot TEO, an improved version of its predecessor Rh-1. The whole platform is conceived under the premise of high efficiency in terms of energy consumption and optimization. We will focus mainly on the electromechanical structure of the lower part of the prototype, which is the main component demanding energy during motion. The dimensions and weight of the robotic platform, together with its link configuration and rigidity, will be optimized. Experimental results are presented to show the validity of the design.

Increasingly disabled and elderly people with mobility problems want to live autonomously in their home environment. They are motivated to use robotic aids to perform tasks by themselves, avoiding permanent nurse or family assistant supervision. They must find means to rehabilitate their abilities to perform daily life activities (DLAs), such as eating, shaving, or drinking. These means may be provided by robotic aids that incorporate possibilities and methods to accomplish common tasks, aiding the user in recovery of partial or complete autonomy. Results are highly conditioned by the system's usability and potential. The developed portable assistive robot ASIBOT helps users perform most of these tasks in common living environments. Minimum adaptations are needed to provide the robot with mobility throughout the environment. The robot can autonomously climb from one surface to another, fixing itself to the best place to perform each task. When the robot is attached to its wheelchair, it can move along with it as a bundle. This paper presents the work performed with the ASIBOT in the area of rehabilitation robotics. First, a brief description of the ASIBOT system is given. A description of tests that have been performed with the robot and several impaired users is given. Insight into how these experiences have influenced our research efforts, especially, in home environments, is also included. A description of the test bed that has been developed to continue research on performing DLAs by the use of robotic aids, a kitchen environment, is given. Relevant conclusions are also included.

Assistive robotics HRI has a number of important characteristics that distinguishes it from other forms of HRI. This includes the need for both high flexibility, safety and reliability in controlling the robotic system. Approaching the system as a human-robot binomial, with the user and the robot acting in a closed-loop, may be beneficial to understanding and improving the interaction. This paper investigates the feasibility of modeling and quantifying assistive robotics HRI inside such a human-robot binomial using concepts from Information Theory.

ASIBOT is an assistive climbing robot that is capable of aiding in daily tasks from fixed docking stations in the environment. A task-oriented design process was applied to improve the robot kinematic structure, which was based on the grid method. Twelve different robot designs were optimized for typical kitchen scenarios, followed by a quantitative comparison.

This paper deals with alternative humanoid robot dynamics modelling, using the screw theory and Lie groups called the special Euclidean group (SE(3)). The dynamic models are deduced analitically. The inverse dynamics model is obtained by the Lagrangian formulation under screw theory, when the Jacobian manipulator depends on the respective twist and joint angles; on the other hand, the POE formula drives a very natural and explicit description of the Jacobian manipulator without the drawbacks of local representation. The forward dynamics were solved by propagation method from an end-effector to the center of gravity (COG) always on the SE(3). Many tests for reference dynamic walking patterns have been carried out, which are represented in simulation and experimental results. The results will be discussed in order to validate the proposed algorithms.

Human care and service demands need an innovative robotic solution to make easier the everyday life of elderly and disabled people in home and workplace environments. The main objective of this project is to develop a new concept of climbing robot for this type of service applications. The robot is a 5 DOF self-containing manipulator, that includes on-board all the control system. The main advantage of the robot is its light weight, about 11 kg for a 1.3m reach. The robot is totally autonomous and needs only power supply to be operated. The robot is a symmetrical arm able to move (or to climb) between different points (docking stations) of the rooms and, if it is necessary, "jump" to (or from) the environment to the wheelchair. In this way the MATS robot should become a home companion and assistance for numerous persons. This paper presents the live experimentation of the robot. Different tasks have been tested such as: eating, shaving, making-up, etc.