To physically interact with a rich variety of environments and situation-oriented requirements, humans continuously adapt both the stiffness and the force of their limbs through antagonistic muscle coactivation. Reflecting this behaviour in prostheses may promote control naturalness and intuitiveness and, consequently, their acceptance in everyday life. We propose a method capable of a simultaneous and proportional decoding of position and stiffness intentions from two surface electro-myographic sensors placed over a pair of antagonistic muscles. First, the algorithm is validated and compared to existing control modalities. Then, the algorithm is implemented in a soft under-actuated prosthetic hand (SoftHand Pro). We investigated the feasibility of our approach in a preliminary study involving one prosthetic user. Our future goal is to evaluate the usability of the proposed approach executing a variety of tasks including physical social interaction with other subjects (see Figure 1). Our hypothesis is that variable stiffness could be a compromise between firm control and safe interaction.
The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between
which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters.
Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot.