A current trend in robotics is to define robot motions so that they can be easily adopted to situations beyond those for which the motion was originally designed. In this work, we show how the challenging task of playing minigolf can be efficiently tackled by first learning a basic hitting motion model, and then learning to adapt it to different situations. We model the basic hitting motion with an autonomous Dynamical Systems (DS), and solve the problem of learning the parameters of the model from a set of demonstrations through a constrained optimization. To hit the ball with the appropriate hitting angle and speed, a nonlinear model of the hitting parameters is estimated based on a set of examples of good hitting parameters. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring the hitting parameters for the minigolf task. We demonstrate the generalization ability of the model in various situations. We validate our approach on the 7 Degrees of Freedom (DoF) Barrett WAM arm and 6-DoF Katana arm in both simulated and real environments.

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.

Abstract—In this paper, we provide an extension to our previous approach [1] to perform obstacle avoidance in the presence of multiple fast moving and rotating obstacles. Our approach leverage on the notion of DS to generate robot motions that are inherently robust to perturbations and can instantly adapt to changes in the target and obstacles ’ positions in a dynamically moving environments. We validate our method in the challenging experiment of dodging a fast moving and rotating box on the 7-degrees of freedom (DoF) KUKA DLR arm. I.

Non-linear dynamical systems (DS) have been used extensively for building generative models of human behavior. Their applications range from modeling brain dynamics to encoding motor commands. Many schemes have been proposed for encoding robot motions using dynamical systems with a single attractor placed at a predefined target in state space. Although these enable the robots to react against sudden perturbations without any re-planning, the motions are always directed towards a single target. In this work, we focus on combining several such DS with distinct attractors, resulting in a multi-stable DS. We show its applicability in reach-to-grasp tasks where the attractors represent several grasping points on the target object. While exploiting multiple attractors provides more flexibility in recovering from unseen perturbations, it also increases the complexity of the underlying learning problem. Here we present the Augmented-SVM (A-SVM) model which inherits region partitioning ability of the well known SVM classifier and is augmented with novel constraints derived from the individual DS. The new constraints modify the original SVM dual whose optimal solution then results in a new class of support vectors (SV). These new SV ensure that the resulting multistable DS incurs minimum deviation from the original dynamics and is stable at each of the attractors within a finite region of attraction. We show, via implementations on a simulated 10 degrees of freedom mobile robotic platform, that the model is capable of real-time motion generation and is able to adapt on-the-fly to perturbations. 1