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

Adaptive Natural Oscillator to Exploit Natural Dynamics for Energy Efficiency

We present a novel adaptive oscillator, called Adaptive Natural Oscillator (ANO), to exploit the natural dynamics of a given robotic system. This tool is built upon the Adaptive Frequency Oscillator (AFO), and it can be used as a pattern generator in robotic applications such as locomotion systems. In contrast to AFO, that adapts to the frequency of an external signal, ANO adapts the frequency of reference trajectory to the natural dynamics of the given system. In this work, we prove that, in linear systems, ANO converges to the system's natural frequency. Furthermore, we show that this tool exploits the natural dynamics for energy efficiency through minimization of actuator effort. This property makes ANO an appealing tool for energy consumption reduction in cyclic tasks; especially in legged systems. We also extend the proposed adaptation mechanism to high dimensional and general cases; such as n-DOF manipulators. In addition, by investigating a hopper leg in simulation, we show the efficacy of ANO in face of dynamical discontinuities; such as those inherent in legged locomotion. Furthermore, we apply ANO to a simulated compliant robotic manipulator performing a periodic task where the energy consumption is drastically reduced. Finally, the experimental results on a 1-DOF compliant joint show that our adaptive oscillator, despite all practical uncertainties and deviations from theoretical models, exploits the natural dynamics and reduces the energy consumption

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

It is well-known that human social interactions generate synchrony phenomena which are often unconscious. If the interaction between individuals is based on rhythmic movements, synchronized and coordinated movements will emerge from the social synchrony. This paper proposes a plausible model of plastic neural controllers that allows the emergence of synchronized movements in physical and rhythmical interactions. The controller is designed with central pattern generators (CPG) based on rhythmic Rowat-Selverston neurons endowed with neuronal and synaptic Hebbian plasticity. To demonstrate the interest of the proposed model, the case of handshaking is considered because it is a very common, both physically and socially, but also, a very complex act in the point of view of robotics, neuroscience and psychology. Plastic CPGs controllers are implemented in the joints of a simulated robotic arm that has to learn the frequency and amplitude of an external force applied to its effector, thus reproducing the act of handshaking with a human. Results show that the neural and synaptic Hebbian plasticity are working together leading to a natural and autonomous synchronization between the arm and the external force even if the frequency is changing during the movement. Moreover, a power consumption analysis shows that, by offering emergence of synchronized and coordinated movements, the plasticity mechanisms lead to a significant decrease in the energy spend by the robot actuators thus generating a more adaptive and natural human/robot handshake

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

Online Learning of Gait Models

Gait event identification is the identification of gait events (e.g., foot impact) which occur cyclically, at the same location in each gait cycle. Gait event identification plays an important role in many applications, such as health monitoring, diagnosis, and rehabilitation. The majority of gait event identification algorithms are based on heuristics, many of which are threshold-based, making them sensitive to threshold parameters and causing poor generalization to new data (e.g., different gait type, ground surface, sensor placement, footwear, etc.). While a number of machine learning techniques have been proposed, they use offline training and do not generalize well to data that is different from the training set. This thesis proposes a novel approach for online, individualized gait analysis, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. The model of gait is learned online during observation, using incremental updates to the model parameters based on the error between the model-predicted and measured signal. The gait data is modeled as a periodic signal with a continuous phase variable, allowing data to be automatically labeled with a phase value corresponding to a particular event that re-occurs each gait cycle. Once the algorithm has converged to the input signal, key gait events can be identified based on the estimated gait phase. Two methods of gait event identification were implemented: analytical event identification and initial event identification. Since we learn a gait model that has an analytical representation, if we know the properties of the gait events of interest, we can use the model to directly compute the corresponding phase. For example, to identify the peak event in each gait cycle, we can solve for the phase which generates the maximum value and assign this as the peak phase value. In the initial event identification method, we provide a manual or heuristic identification of a gait event in the first converged gait cycle and use the corresponding event phase to identify all future events. Once gait events are identified relative to the estimated gait phase, we can automatically identify any future events since we assume they occur at the same phase, as is common in gait analysis. Our approach is implemented and tested on two datasets: one measuring medio lateral angular velocity of the ankles from a healthy young group of adults and the other measuring sagittal linear acceleration of the ankles from a group of retirement home residents who each have a variety of medical conditions. For the former dataset, the proposed approach converges within approximately five gait cycles and heel impact and toe takeoff events are extracted with an average error of 0.04 gait cycles, using the manual initial event identification method. For the latter dataset, the proposed approach converges within approximately eight gait cycles and initial swing events are extracted with an average error of 0.03 gait cycles, using the analytical event identification method. When using learning rates optimized on a set of training trials (opposed to a default set of learning rates), the proposed approach converges within approximately four gait cycles and maintains an average error of 0.03 gait cycles, on the corresponding set of test trials. Further, when including ground truth events occurring prior to the model having met convergence criteria, the average error is only slightly increased to 0.04 gait cycles

On-line frequency adaptation and movement imitation for rhythmic robotic tasks

In this paper we present a novel method to obtain the basic frequency of an unknown periodic signal with an arbitrary waveform, which can work online with no additional signal processing or logical operations. The method originates from non-linear dynamical systems for frequency extraction, which are based on adaptive frequency oscillators in a feedback loop. In previous work, we had developed a method that could extract separate frequency components by using several adaptive frequency oscillators in a loop, but that method required a logical algorithm to identify the basic frequency. The novel method presented here uses a Fourier series representation in the feedback loop combined with a single oscillator. In this way it can extract the frequency and the phase of an unknown periodic signal in real time and without any additional signal processing or preprocessing. The method determines the Fourier series coefficients and can be used for dynamic Fourier series implementation. The proposed method can be used for the control of rhythmic robotic tasks, where only the extraction of the basic frequency is crucial. For demonstration several highly non-linear and dynamic periodic robotic tasks are shown, including also a task where an electromyography (EMG) signal is used in a feedback loop