The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a model-based evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring the coupled system stability. The experiment consists of a hardware-in-the-loop optimization and a one-degree-of-freedom robot used for wrist rehabilitation. Experimental results showed that the proposed technique could generate customized passive impedance controllers for three subjects. Furthermore, when compared with a constant impedance controller, the method suggested decreased in 20% the root mean square of interaction torques while maintaining stability during optimization.

Although the design of legged robots may be dependent on the application, all of them share the need to safely deal with physical interaction with the environment in every step they take. Impedance controllers have been applied with success to handle contact, and some authors applied the concept of passivity to guarantee stability during the interaction. Whereas previous studies on the passivity of legged robots considered aspects such as inner force loop gains and actuation bandwidth influence on the Z-width (i.e. the range of renderable passive impedances), they did not take into account the role of the kinematic configuration of the leg on the stability of the interaction. Thus, in this work we present a systematic analysis of the effects of joint positions on the passivity conditions of a robotic leg and show that this is a very relevant aspect that may seriously affect the stability and passivity of an impedance controller. By analyzing a linearized model of the leg via its Nyquist plots and the respective Z-width diagrams, we were able to determine what joint configurations within the leg workspace are more suitable to physically interact with the environment or people.

Fasteners assembly automation in different industries require flexible systems capable of dealing with faulty situations. Fault detection and isolation (FDI) techniques are used to detect failure and deal with them, avoiding losses on parts, tools or robots. However, FDI usually deals with the faults after or at the moment they occur. Thus, we propose a method that predicts potential failures online, based on the forces and torques signatures captured during the task. We demonstrate the approach experimentally using an industrial robot, equipped with a force-torque sensor and a pneumatic gripper, used to align and thread nuts into bolts. All effort information is fed into a supervised machine learning algorithm, based on a Convolutional Neural Network (CNN) classifier. The network was able to predict and classify the threading task outcomes in 3 groups: mounted, not mounted or jammed. Our approach was able to reduce in 10.9% the threading task execution time when compared to a reference without FDI, but had problem to predict jammed cases. The same experiment was also performed with other two additional learning algorithms, and the results were systematically compared.

To render a wearable robot imperceptible to a user is a very challenging control task. The constant and intrinsic interaction between robot and human, and person-dependent behaviours are the main difficulties when designing such cooperative control. In this contribution we introduce and discuss a novel and promising transparency control framework. The foundation of the framework is to measure the acceleration of the human limbs and to exploit this measurement to generate feedforward control commands by using a rigid body model of the robot. The framework includes also an acceleration feedback controller and a state estimator to enhance the overall performance. We present a simplified stability analysis with different feedback controllers and preliminary experimental data that demonstrate the potential of the proposed method in reducing interaction forces and mimicking human motions.

Increasingly, robots are designed to interact with the environment, including humans and tools. Legged robots, in particular, have to deal with environmental contacts every time they take a step. To handle these interactions properly, it is desirable to be able to set the robot's dynamic behavior, i.e., its impedance. In this contribution, we investigate the most relevant theoretical and practical aspects in impedance control using hydraulic actuators, ranging from the force dynamics analysis and model-based controller design to the overall stability and performance assessment. We present results with one leg of the quadruped robot HyQ and also highlight the influence of hardware parameters, such as valve bandwidth and inertia, in the impedance and force tracking. In addition, we demonstrate the capabilities of HyQ's actively compliant leg by experimentally comparing it with a passively compliant version of the same leg. With such a broad spectrum of analyses and discussions, this paper aims to serve as a practical and comprehensive guide for implementing high-performance impedance control on highly dynamic hydraulic robots.

A legged robot has to deal with environmental contacts every time it takes a step. To properly handle these interactions, it is desirable to be able to set the foot compliance. For an actively-compliant legged robot, in order to ensure a stable contact with the environment the robot leg has to be passive at the contact point. In this work, we asses some passivity and stability issues of the actively-compliant leg of the quadruped robot HyQ, which employs a highperformance cascade compliance controller. We demonstrate that both the nested torque loop performance as well as the actuator bandwidth have a strong influence in the range of virtual impedances that can be passively rendered by the robot leg. Based on the stability analyses and experimental results, we propose a procedure for designing cascade compliance controllers. Furthermore, we experimentally demonstrate that the HyQ's actively-compliant leg is able to reproduce the compliant behavior presented by an identical but passively-compliant version of the same leg.

Legged robots have the potential to serve as versatile and useful autonomous robotic platforms for use in unstructured environments such as disaster sites. They need to be both capable of fast dynamic locomotion and precise movements. However, there is a lack of platforms with suitable mechanical properties and adequate controllers to advance the research in this direction. In this paper we are presenting results on the novel research platform HyQ, a torque controlled hydraulic quadruped robot. We identify the requirements for versatile robotic legged locomotion and show that HyQ is fulfilling most of these specifications. We show that HyQ is able to do both static and dynamic movements and is able to cope with the mechanical requirements of dynamic movements and locomotion, such as jumping and trotting. The required control, both on hydraulic level (force/torque control) and whole body level (rigid model based control) is discussed.

Robots are frequently modeled as rigid body systems, having torques as input to their dynamics. A high-performance low-level torque source allows us to control the robot/environment interaction and to straightforwardly take advantage of many model-based control techniques. In this paper, we define a general 1-DOF framework, using basic physical principles, to show that there exists an intrinsic velocity feedback in the generalized force dynamics, independently of the actuation technology. We illustrate this phenomena using three different systems: a generic spring-mass system, a hydraulic actuator, and an electric motor. This analogy helps to clarify important common aspects regarding torque/force control that can be useful when designing and controlling a robot. We demonstrate, using simulations and experimental data, that it is possible to compensate for the load motion influence and to increase the torque tracking capabilities.

This paper is focussed on the modelling and control of a hydraulically-driven biologically-inspired robotic leg. The study is part of a larger project aiming at the development of an autonomous quadruped robot (hyQ) for outdoor operations. The leg has two hydraulically-actuated degrees of freedom (DOF), the hip and knee joints. The actuation system is composed of proportional valves and asymmetric cylinders. After a brief description of the prototype leg, the paper shows the development of a comprehensive model of the leg where critical parameters have been experimentally identified. Subsequently the leg control design is presented. The core of this work is the experimental assessment of the pros and cons of single-input single-output (SISO) vs. multiple-input multiple-output (MIMO) and linear vs. nonlinear control algorithms in this application (the leg is a coupled multivariable system driven by nonlinear actuators). The control schemes developed are a conventional PID (linear SISO), a Linear Quadratic Regulator (LQR) controller (linear MIMO) and a Feedback Linearisation (FL) controller (nonlinear MIMO). LQR performs well at low frequency but its behaviour worsens at higher frequencies. FL produces the fastest response in simulation, but when implemented is sensitive to parameters uncertainty and needs to be properly modified to achieve equally good performance also in the practical implementation.