Rehabilitation is an area of robotics in which human–robot collaboration occurs, requiring adaptation and compliance. Pneumatic artificial muscles (PAM) are soft actuators that have built-in compliance making them usable for rehabilitation robots. Conversely, arises from nonlinear characteristics generates obstructions modeling controlling actions. It a critical issue limiting the use PAM. In t...

This paper presents a novel whole-body analytical inverse kinematics (IK) method integrating collision avoidance and customizable body control for animating reaching tasks in real-time. Whole-body control is achieved with the interpolation of pre-designed key body postures, which are organized as a function of the direction to the goal to be reached. Arm postures are computed by the analytical ...

Numerical optimal control (the approximation of an optimal trajectory using numerical iterative algorithms) is a promising approach to compute the control of complex dynamical systems whose instantaneous linearization is not meaningful. Aside from the problems of computation cost, these methods raise several conceptual problems, like stability, robustness, or simply understanding of the nature ...

One common approach to imitation learning is behavioral cloning (BC), which employs straightforward supervised learning (i.e., classification) to directly map observations to controls. A second approach is inverse optimal control (IOC), which formalizes the problem of learning sequential decision-making behavior over long horizons as a problem of recovering a utility function that explains obse...

New computational tools become available at every faster rates. A fundamental question asks how such tools can help achieve clearly defined learning objectives. This article argues, in a sequence of examples, that computing technologies can much support the implementation of modern pedagogy. The focus is on enhancing a learner centered environment. Of critical importance is that the learner tak...

A plant can track an input command signal if it is driven by a controller whose transfer function approximates the inverse of its transfer function. A stable inverse can be obtained even if the plant is nonminimum-phase. No direct feedback is used, except that the plant output is monitored and utilized to adapt the parameters of the controller. A model-reference inverse control system can learn...

The purpose of this paper is to present inverse optimal control as a promising approach to transfer biological motions to robots. Inverse optimal control helps (a) to understand and identify the underlying optimality criteria of biological motions based on measurements, and (b) to establish optimal control models that can be used to control robot motion. The aim of inverse optimal control probl...

Model based technologies form the core of advanced robotic applications such as model predictive control and feedback linearization. More sophisticated models result in higher quality but the use in embedded real-time control systems imposes strict requirements on timing, memory allocation, and robustness. To satisfy these constraints, the model implementation is often optimized by manual codin...