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.

Impedance control is commonly implemented for robotic contact applications. Its performance is a function of the dynamic coupling between the environment and the robot's impedance controller: inertia, stiffness and damping. An interaction task may be considered successfully accomplished when the elected performance criteria, such as rise time, overshoot and accommodation are achieved, all in addition to guaranteeing stability. The selection and online changes of impedance parameters for such purpose is considered challenging for practical applications, given the non modeled dynamics of the robot and its intrinsic impedance, uncertainties in the dynamics of environment and tool. This study proposes the usage of a multi-objective genetic algorithm to obtain enhanced impedance controller gains, with the purpose of providing impedance control adaptability to a robot in a real physical interaction application. In this context, a one-degree-of-freedom mechanical contact model is proposed to obtain the values of the objective function. A genetic algorithm is used to obtain the best gains, as this is a nonlinear task. Simulation and experimental results are presented and discussed validating the system performance, which has shown a convergence of less than 6 generations. Tendencies to convergence of controller parameters were observed and discussed.

An incremental self-organizing map, called State Trajectory Generator (STRAGEN) is employed to plan state trajectories of a robot. STRAGEN can deal with different criteria to construct topological maps of the problem space, choosing neighbors that match these criteria and optimize different measures of the learned map. STRAGEN can also learn heterogeneous information, such as angles, torques and positions of a manipulator, preserving their characteristics. This algorithm was tested by generating trajectories for a robotic hand called Kanguera. Kanguera presents a new concept of anthropomorphic robot hand. The hand offers a suitable environment for experimental purposes due to its novel and more accurate transmission system. The implementation of adduction and abduction capacity for both the fingers and the thumb allows the execution of more complex movements. Simulations and experiments related to Kanguera hardware are also presented.

This work presents a manipulation strategy for a anthropomorphic robotic hand based on the use of distinct model topologies and the analytical solution for the inverse kinematics. To evaluate the approach a pencil handling example is implemented using the Brazilian Anthropomorphic Hand - BRAHMA, a five fingers artificial robotic hand with 22 degree of freedom. Additionally a state space position control for the joints was implemented. The non-linear model is piece-wise linearized and linear models are used to design the control. The manipulation strategy is tested imposing a movement to the pencil that the BRAHMA controller and the results are presented through numeric simulation.

This paper presents a complete planning and control strategy to the manipulation of objects and tools using an anthropomorphic gripper. To approximate, in an efficient way, the functions accomplished by a human hand, the controller was conceived taking into consideration: the gripper geometry, the gripper elastic characteristics, the object geometry and the intermittent contacts possibility. To take the gripper and object characteristics into consideration the screw theory was adopted. The screw theory allows the analysis in the three-dimensional Euclidean space of six-dimensional complex problems. The planning task is accomplished using an artificial neural network based system, suitable for real-time application.

Presents a neural system to determine three contact points between a gripper and an object of arbitrary shape. The neural system is composed of three functional blocks to capture and process the image, establish the contact points and estimate the contact forces. The second block is formed by two neural networks. The first network (competitive Hopfield neural network) determines an approximate polygon for an object outline. A second network, a RBF or MLP model, defines three contact points. The results suggest that the neural system always reaches stable grasping for known and unknown objects Moreover, the training methods used by the RBF model influences significantly the performance and the learning speed of the system.

This paper presents a two-stage neural system to determine the contact points between a three-fingered gripper and an object of arbitrary shape. In the first stage, a CCD camera captures the image of the object and such an image is transformed into a two-dimensional outline through a nearest neighbour algorithm. In the second phase, two neural networks, functioning in cascade, select three contact points in the outline. A competitive Hopfield neural network defines an approximate polygon considering a reduced number of boundary points of the original outline. Then, a supervised neural network, either a multi-layer perceptron or a radial basis function (RBF) network, find the contact points. The experiments suggest that the RBF network trained by the global ridge regression method is suitable for on-line applications and presents the best overall performance in terms of accuracy and robustness to noise. Moreover, this method is able to find correctly the contact points for objects of arbitrary shapes.