Abstract—This paper presents a method to learn discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e., time-invariant) dynamical system (DS) and define sufficient conditions to ensure global asymptotic stability at the target. We propose a learning method, which is called Stable Estimator of Dynamical Systems (SEDS), to learn the parameters of the DS to ensure that all motions closely follow the demonstrations while ultimately reaching and stopping at the target. Timeinvariance and global asymptotic stability at the target ensures that the system can respond immediately and appropriately to perturbations that are encountered during the motion. The method is evaluated through a set of robot experiments and on a library of human handwriting motions. Index Terms—Dynamical systems (DS), Gaussian mixture model, imitation learning, point-to-point motions, stability analysis. I.

with respect to the learning parameters

Abstract — One of the hallmarks of physical interaction between humans is haptic communication, i.e. an information exchange through force signals. Humans excel in tasks that require such interaction by adapting impedance and anticipating the partner’s intentions. It is highly desirable to endow robots with similar capabilities. Recently, the robotics community renewed its interest in variable impedance control. A special emphasis is put on the development of controllers that incorporate learning as an essential element. This article combines programming by demonstration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. Learning a task model allows the robot to anticipate the partner’s intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compensate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm which tunes the impedance parameters, so as to ensure accurate reproduction. To simplify the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained in physically-realistic simulation of a dyad of two planar 2-DOF robots. realistic simulation 1; see Figure 1-(b)-(d). We will further denote as robot-leader the robot that substitutes the human in the real-world experiments, while the other robot will be denoted as robot-follower. The robot-follower proactively changes the reference trajectory to synchronize with the robot-leader, which keeps its reference trajectory unchanged. I.

dynamical system based hand-arm grasp

journal homepage: www.elsevier.com/locate/robot Coupled dynamical system based arm–hand grasping model for learning fast

Abstract—A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a nontrivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment. I.