Modeling discrete and rhythmic movements through motor primitives: a review

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the wor

Movement generation using dynamical systems : a humanoid robot performing a drumming task

The online generation of trajectories in humanoid robots remains a difficult problem. In this contribution, we present a system that allows the superposition, and the switch between, discrete and rhythmic movements. Our approach uses nonlinear dynamical systems for generating trajectories online and in real time. Our goal is to make use of attractor properties of dynamical systems in order to provide robustness against small perturbations and to enable online modulation of the trajectories. The system is demonstrated on a humanoid robot performing a drumming task.This work was made possible thanks to the support of the Swiss National Science Foundation (A.I.) and of the European Commissions Cognition Unit, project no. IST-2004-004370 : RobotCub (S.D.

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching

Hand placement during quadruped locomotion in a humanoid robot: A dynamical system approach

Locomotion on an irregular surface is a challenging task in robotics. Among different problems to solve to obtain robust locomotion, visually guided locomotion and accurate foot placement are of crucial importance. Robust controllers able to adapt to sensory-motor feedbacks, in particular to properly place feet on specific locations, are thus needed. Dynamical systems are well suited for this task as any online modification of the parameters leads to a smooth adaptation of the trajectories,allowing a safe integration of sensory-motor feedback. In this contribution, as a first step in the direction of locomotion on irregular surfaces, we present a controller that allows hand placement during crawling in a simulated humanoid robot. The goal of the controller is to superimpose rhythmic movements for crawling with discrete (i.e. short-term) modulations of the hand placements to reach specific marks on the ground

On the influence of symbols and myths in the responsibility ascription problem in roboethics - A roboticist’s perspective

Because of the increasing developments of humanoid robots, humans and robots are going to interact more and more often in the near future. Thus, the need for a well-defined ethical framework in which these interactions will take place is very acute. In this article, we will show why responsibility ascription is a key concept to understand today’s and tomorrow’s ethical issues related to human-robot interactions. By analyzing how the myths surrounding the figure of the robot in western societies have been built through centuries, we will be able to demonstrate that the question of responsibility ascription is biased in the sense that it assigns to autonomous robots a role that should be devoted to human

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

Offline Decoding of Upper Limb Muscle Synergies from EEG Slow Cortical Potentials

Muscle synergies are thought to be the building blocks used by the central nervous system to control the underdetermined problem of muscles activation. Decoding these synergies from EEG could provide useful tools for BCI-controlled orthotic devices. In this paper, we assess the possibility of decoding muscle synergies from EEG slow cortical potentials in two healthy subjects and two stroke patients performing a center-out reaching task. We were able to successfully decode the extracted muscle synergies in both healthy subject and one patient

A modular bio-inspired architecture for movement generation for the infant-like robot iCub

Movement generation in humans appears to be processed through a three-layered architecture, where each layer corresponds to a different level of abstraction in the representation of the movement. In this article, we will present an architecture reflecting this three-layered organization and based on a modular approach to human movement generation. We will show that our architecture is well suited for the online generation and modulation of motor behaviors, but also for switching between motor behaviors. This will be illustrated respectively through an interactive drumming task and through switching between reaching and crawling