This article presents a novel control framework for assisting human arm movements with a robotic device. Unlike existing collaborative and wearable robots that predominantly operate in task space or joint space, our framework focuses on controlling variables related to the muscular space, which refers to the activation and force generation of individual muscles. Translating these variables to the robot's actuation space is challenging, which hinders the development of human-centered tasks and limits the transparency of robotic assistance for the central nervous system. To address these issues, we consider the relationship between the muscular space and the robot's task space, using the kinematics and dynamics of the human limb obtained from a calibrated musculoskeletal model based on Hill's muscle model. Our framework includes two muscle-targeted methods: assistance and augmentation. The assistance method allows exercise or rehabilitation of specific muscles, while the augmentation method achieves isotropic manipulability, enabling the user to exert forces uniformly in all directions using the novel concept of a force envelope. The experiments were conducted using a haptic robot in admittance mode with a viscous environment simulating a load. The results demonstrate significant improvement in the prediction capabilities of the calibrated model compared with the baseline. Muscle-targeted assistance induces substantial changes in targeted muscular efforts. In addition, we achieve isotropic manipulability in a more precise way as compared with previous methods. In summary, our proposed control framework proves effective in assisting human arm movements with a robotic device for rehabilitation and power augmentation in human-centered applications.

While existing literature encompasses exoskeleton-assisted sit-stand tasks, the integration of energy recovery mechanisms remains unexplored. To push the boundaries further, this study introduces a portable pneumatic knee exoskeleton that operates in both quasi-passive and active modes, where active mode is utilized for aiding in standing up (power generation), thus the energy flows from the exoskeleton to the user, and quasi-passive mode for aiding in sitting down (power absorption), where the device absorbs and can store energy in the form of compressed air, leading to energy savings in active mode. The absorbed energy can be stored and later reused without compromising exoskeleton transparency in the meantime. In active mode, an air pump inflates the pneumatic artificial muscle (PAM), which stores the compressed air, that can then be released into a pneumatic cylinder to generate torque. All electronic and pneumatic components are integrated into the system, and the exoskeleton weighs 3.9 kg with a maximum torque of 20 Nm at the knee joint. The paper describes the mechatronic design, mathematical model and includes a pilot study with an able-bodied subject performing sit-to-stand tasks. The results show that the exoskeleton can recover energy while assisting the subject and reducing mean muscle activity by ~31%. Further results highlight air regeneration’s potential for energy saving in portable pneumatic exoskeletons, showing that the proposed device extends exoskeleton operation by ~27%.

This paper presents a study on the use of the Talos humanoid robot for performing assistive sit-to-stand or stand-to-sit tasks. In such tasks, the human exerts a large amount of force (100–200 N) within a very short time (2–8 s), posing significant challenges in terms of human unpredictability and robot stability control. To address these challenges, we propose an approach for finding a spatial reference for the robot, which allows the robot to move according to the force exerted by the human and control its stability during the task. Specifically, we focus on the problem of finding a 1D manifold for the robot, while assuming a simple controller to guide its movement on this manifold. To achieve this, we use a functional representation to parameterize the manifold and solve an optimization problem that takes into account the robot's stability and the unpredictability of human behavior. We demonstrate the effectiveness of our approach through simulations and experiments with the Talos robot, showing robustness and adaptability.

Autonomous trajectory generation through generalization requires a database of motion, which can be difficult and time consuming to obtain. In this paper, we propose a method for autonomous expansion of a database for the generation of compliant and accurate motion, achieved through the framework of compliant movement primitives (CMPs). These combine task-specific kinematic and corresponding feed-forward dynamic trajectories. The framework allows for generalization and modulation of dynamic behavior. Inspired by human sensorimotor learning abilities, we propose a novel method that can autonomously learn task-specific torque primitives (TPs) associated to given kinematic trajectories, encoded as dynamic movement primitives. The proposed algorithm is completely autonomous, and can be used to rapidly generate and expand the CMP database. Since CMPs are parameterized, statistical generalization can be used to obtain an initial TP estimate of a new CMP. Thereby, the learning rate of new CMPs can be significantly improved. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a peg-in-hole task shows fast TP acquisition and accurate generalization estimates in real-world scenarios.

The paper presents a novel control method for the arm exoskeletons that takes into account the muscular force manipulability of the human arm. In contrast to classical controllers that provide assistance without considering the biomechanical properties of the human arm, we propose a control method that takes into account the configuration of the arm and the direction of the motion to effectively compensate the anisotropic property of the muscular manipulability of the human arm. Consequently, the proposed control method effectively maintains a spherical endpoint manipulability in the entire workspace of the arm. As a result, the proposed method allows the human using the exoskeleton to efficiently perform tasks in arm configurations that are normally unsuitable due to the low manipulability. We evaluated the proposed approach by a preliminary experimental study where a subject wearing a 2 DOF arm-exoskeleton had to move a 4 kg weight between several locations. The results of our study demonstrate that the proposed approach effectively augments the ability of human motor control to perform tasks equally well in the whole arm workspace that include configurations with low intrinsic manipulability.

In this paper we propose a novel control approach for robots that are physically coupled with humans, such as exoskeletons and assisting devices. In contrast with the conventional controllers, where assistance is usually provided indifferently of the configuration of the arm and direction of the motion, we propose a control method that compensates the anisotropic property of the manipulability related to the human arm. Consequently, the assistive behaviour of the proposed method allows the user to perform tasks in arm configurations that are otherwise unsuitable due to the lack of manipulability. In effect, the proposed method transforms the elliptic shape of the force manipulability ellipse to a circular shape in the whole space of human arm configurations. The proposed approach was evaluated by the arm-reaching task that involved pushing of a heavy object on a plane. We tested the accuracy and efficiency of the proposed method under several conditions that involved motions with added weight and friction. The results of our study show that the proposed approach significantly improves the human motor control ability and maintains the desired accuracy of the movement.

In this paper we propose a human-in-the-loop approach for teaching robots how to solve part assembly tasks. In the proposed setup the human tutor controls the robot through a haptic interface and a hand-held impedance control interface. The impedance control interface is based on a linear spring-return potentiometer that maps the button position to the robot arm stiffness. This setup allows the tutor to modulate the robot compliance based on the given task requirements. The demonstrated motion and stiffness trajectories are encoded using Dynamical Movement Primitives and learnt using Locally Weight Regression. To validate the proposed approach we performed experiments using Kuka Light Weight Robot and HapticMaster robot. The task of the experiment was to teach the robot how to perform an assembly task involving sliding a bolt fitting inside a groove in order to mount two parts together. Different stiffness was required in different stages of the task execution to accommodate the interaction of the robot with the environment and possible human-robot cooperation.

In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration cycle signals in contrast to the standard ILC, which uses signals from the previous iteration cycle. This reduces the computational burden and enhances the adaptation speed. Stability of the proposed control law is analysed and discussed. The proposed approach is evaluated in simulation and on a Kuka Light Weight Robot Arm where the task was to perform force-based surface following with both discrete and periodic movements.

The creation and adaptation of motor behaviors is an important capability for autonomous robots. In this paper we propose an approach for altering existing robot behaviors online, where a human coach interactively changes the robot motion to achieve the desired outcome. Using hand gestures, the human coach can specify the desired modifications to the previously acquired behavior. To preserve a natural posture while performing the task, the movement is encoded in the robot's joint space using periodic dynamic movement primitives. The coaching gestures are mapped to the robot joint space via robot Jacobian and used to create a virtual force field affecting the movement. A recursive least squares technique is used to modify the existing movement with respect to the virtual force field. The proposed approach was evaluated on a simulated three degrees of freedom planar robot and on a real humanoid robot, where human coaching gestures were captured by an RGB-D sensor. Although our focus was on rhythmic movements, the developed approach is also applicable to discrete (point-to-point) movements.

Dynamic movement primitives (DMPs) were proposed as an efficient way for learning and control of complex robot behaviors. They can be used to represent point-to-point and periodic movements and can be applied in Cartesian or in joint space. One problem that arises when DMPs are used to define control policies in Cartesian space is that there exists no minimal, singularity-free representation of orientation. In this paper we show how dynamic movement primitives can be defined for non minimal, singularity free representations of orientation, such as rotation matrices and quaternions. All of the advantages of DMPs, including ease of learning, the ability to include coupling terms, and scale and temporal invariance, can be adopted in our formulation. We have also proposed a new phase stopping mechanism to ensure full movement reproduction in case of perturbations.

In this paper we address the problem of accurate trajectory tracking while ensuring compliant robotic behaviour for periodic tasks. We propose an approach for on-line learning of task-specific dynamics, i.e. task specific movement trajectories and corresponding force/torque profiles. The proposed control framework is a multi-step process, where in the first step a human tutor shows how to perform the desired periodic task. A state estimator based on an adaptive frequency oscillator combined with dynamic movement primitives is employed to extract movement trajectories. In the second step, the movement trajectory is accurately executed in the controlled environment under human supervision. In this step, the robot is accurately tracking the acquired movement trajectory, using high feedback gains to ensure accurate tracking. Thus it can learn the corresponding force/torque profiles, i. e. task-specific dynamics. Finally, in the third step, the movement is executed with the learned feedforward task-specific dynamic model, allowing for low position feedback gains, which implies compliant robot behaviour. Thus, it is safe for interaction with humans or the environment. The proposed approach was evaluated on a Kuka LRW robot performing object manipulation and crank turning.

Direct transfer of human motion trajectories to humanoid robots does not result in dynamically stable robot movements due to the differences in human and humanoid robot kinematics and dynamics. We developed a system that converts human movements captured by a low-cost RGB-D camera into dynamically stable humanoid movements. The transfer of human movements occurs in real-time. As need arises, the developed system can smoothly transition between unconstrained movement imitation and imitation with balance control, where movement reproduction occurs in the null space of the balance controller. The developed balance controller is based on an approximate model of the robot dynamics, which is sufficient to stabilize the robot during on-line imitation. However, the resulting movements cannot be guaranteed to be optimal because the model of the robot dynamics is not exact. The initially acquired movement is therefore subsequently improved by model-free reinforcement learning, both with respect to the accuracy of reproduction and balance control. We present experimental results in simulation and on a real humanoid robot.

We address the problem of extracting the fundamental frequency of an arbitrary periodic or quasi-periodic signal for application in robotic tasks. We focus on controlling periodic robotic movement by extracting the frequency of human movement by using surface electromyography (EMG), a technique by which muscle action potentials are gathered by electrodes placed on the skin. However, since the EMG signal is quasi-periodic with a lot of frequency components and noise, it is difficult to determine the frequency and phase of the measured limb motion. We propose to use nonlinear dynamical systems capable of extracting the frequency and the phase from an unknown periodic signal with an arbitrary waveform. The method uses a whole Fourier series representation in a feedback loop. It is capable of extracting the frequency and the phase of an unknown periodic signal in real-time, without any additional signal processing or preprocessing. Combining this method with an output dynamic system based on dynamic movement primitives, which generate the desired trajectory, allows synchronization between human muscles actions and some other system actions like robot motion or electrical neuromuscular stimulator activation.

In this paper we propose a novel method for ensuring stability of a skiing robot on a previously unknown ski slope. The method is based on a null-space velocity control algorithm, which is essential for achieving good behavior of redundant robotic systems. Using the pseudo-inverse, the task and the null-space motions are decoupled. For the skiing robot, the primary task is stability, the secondary task is the direction and the third task is the pose of the robot. The dynamic stability index of the skiing robot is defined with the normalized zero moment point. We demonstrated the effectiveness of the proposed method both in simulation in the virtual reality environment as well as on the real robot.