A Review of Compliant Movement Primitives

Dynamical models of robots performing tasks in contact with objects or the environment are difficult to obtain. Therefore, different methods of learning the dynamics of tasks have been proposed. In this chapter, we present a method that provides the joint torques needed to execute a task in a compliant and at the same time accurate manner. The presented method of compliant movement primitives (CMPs), which consists of the task kinematical and dynamical trajectories, goes beyond mere reproduction of previously learned motions. Using statistical generalization, the method allows to generate new, previously untrained trajectories. Furthermore, the use of transition graphs allows us to combine parts of previously learned motions and thus generate new ones. In the chapter, we provide a brief overview of this research topic in the literature, followed by an in-depth explanation of the compliant movement primitives framework, with details on both statistical generalization and transition graphs. An extensive experimental evaluation demonstrates the applicability and the usefulness of the approach

Gaming controllers for research robots: controlling a humanoid robot using a WIIMOTE

In this article we describe the use of a standard game console joystick, namely the Nintendo Wiimote, for controlling a HOAP-2 humanoid robot. We give a short overview on the use of tangible user interfaces, followed by the description of the used game controller and the measurements of some of its characteristics. We show the ease of applicability of inexpensive and robust standard game controllers for direct translation between the user’s intent and the robot actions on a two-handed robot drumming task

Dynamical system for learning the waveform and frequency of periodic signals - application to drumming

The paper presents a two-layered system for learning and encoding a periodic signal and its application to a drumming task. The two layers are the dynamical system responsible for extracting the main frequency of the input signal, based on adaptive frequency oscillators, and the dynamical system responsible for learning of the waveform with a built in learning algorithm. By combining the two dynamical systems we can rapidly teach new trajectories to robots. The systems works online for any periodic signal, requires no signal processing and can be applied in parallel to multiple dimensions. Furthermore, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, and is computationally inexpensive. The algorithm is demonstrated in a drumming task using the HOAP-2 humanoid robot

Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper, we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion and, thus, modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of DMPs, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task

Real-time full body motion imitation on the COMAN humanoid robot

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle. © 2014 IEEE