Cristiano Alessandro

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well.

ECCE1: The first of a series of anthropomimetic musculoskeletal upper torsos

The human body was not designed by engineers and the way in which it is built poses enormous control problems. Its complexity challenges the ability of classical control theory to explain human movement as well as the development of human motor skills. It is our working hypothesis that the engineering paradigm for building robots places severe limitations on the kinds of interactions such robots can engage in, on the knowledge they can acquire of their environment, and therefore on the nature of their cognitive engagement with the environment. This paper describes the design of an anthropomimetic humanoid upper torso, ECCE1, built in the context of the ECCEROBOT project. The goal of the project is to use this platform to test hypotheses about human motion as well as to compare its performance with that of humans, whether at the mechanical, behavioural or cognitive level.

Toward anthropomimetic robotics: Development, simulation, and control of a musculoskeletal torso

Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, were developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.

Towards anthropomimetic robotics: Development, simulation, and control of a musculoskeletal torso

Abstract Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, have been developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.

A computational analysis of motor synergies by dynamic response decomposition

Analyses of experimental data acquired from humans and other vertebrates have suggested that motor commands may emerge from the combination of a limited set of modules. While many studies have focused on physiological aspects of this modularity, in this paper we propose an investigation of its theoretical foundations. We consider the problem of controlling a planar kinematic chain, and we restrict the admissible actuations to linear combinations of a small set of torque profiles (i.e., motor synergies). This scheme is equivalent to the time-varying synergy model, and it is formalized by means of the dynamic response decomposition (DRD). DRD is a general method to generate open-loop controllers for a dynamical system to solve desired tasks, and it can also be used to synthesize effective motor synergies. We show that a control architecture based on synergies can greatly reduce the dimensionality of the control problem, while keeping a good performance level. Our results suggest that in order to realize an effective and low-dimensional controller, synergies should embed features of both the desired tasks and the system dynamics. These characteristics can be achieved by defining synergies as solutions to a representative set of task instances. The required number of synergies increases with the complexity of the desired tasks. However, a possible strategy to keep the number of synergies low is to construct solutions to complex tasks by concatenating synergy-based actuations associated to simple point-to-point movements, with a limited loss of performance. Ultimately, this work supports the feasibility of controlling a non-linear dynamical systems by linear combinations of basic actuations, and illustrates the fundamental relationship between synergies, desired tasks and system dynamics.

Synthesis and Adaptation of Effective Motor Synergies for the Solution of Reaching Tasks

Taking inspiration from the hypothesis of muscle synergies, we propose a method to generate open loop controllers for an agent solving point-to-point reaching tasks. The controller output is defined as a linear combination of a small set of predefined actuations, termed synergies. The method can be interpreted from a developmental perspective, since it allows the agent to autonomously synthesize and adapt an effective set of synergies to new behavioral needs. This scheme greatly reduces the dimensionality of the control problem, while keeping a good performance level. The framework is evaluated in a planar kinematic chain, and the quality of the solutions is quantified in several scenarios.

Muscle Synergies in Clinical Practice: Theoretical and Practical Implications

Understanding how the CNS copes with the redundancy of the musculoskeletal system is a central aim in motor neuroscience and has important implications in the clinical scenario. A long-standing idea hypothesis is that motor control may be simplified by a modular organization, in which several a few muscle synergies are used to organize muscles in functional groups. In this chapter, we present the theory hypothesis of muscle synergies under from a simplified point of view and we describe its practical implications in the context of neurological pathologies. This chapter wants to be an intuitive and practical guide to those practitioners, new to the concept of muscle synergies, willing to understand how to perform such an analysis in typical clinical settings.

Motor Control and Learning Theories

Patients who have suffered impairment of their neuromotor abilities due to a disease or accident have to relearn to control their bodies. For example, after stroke the ability to coordinate the movements of the upper limb in order to reach and grasp an object could be severely damaged. Or in the case of amputees, the functional ability is completely lost.

LEARNING HIGH-LEVEL SENSORS FROM REFLEXES VIA SPIKING NETWORKS IN ROVING ROBOTS

Abstract In this paper we introduce a network of spiking neurons for navigation control. First, the robot is equipped with a system of spiking neurons able to avoid obstacles. Then, a second layer is designed with the aim of providing the robot with a target approaching system, able to direct the robot itself towards visual targets. In both cases we assume that the robot knows some a priori response to low level sensors (i.e. to contact sensors in the case of obstacles or to proximity target sensors in the case of targets) and has to learn the response to high level stimuli (i.e. distance sensors or visual input). Spike-timing-dependent-plasticity (STDP) is used to make the system able to learn high level responses.

Impact of Body Parameters on Dynamic Movement Primitives for Robot Control

Abstract The problem of movement coordination in large DoF (Degree of Freedom) robots is complex due to redundancies. In this regard, Dynamic Movement Primitive (DMP) is a useful planning technique, inspired by biology, that can be used to store and reproduce trajectories about every DoF. This work is a preliminary study that aims to understand and quantify the influence of the robot dynamics upon the performance of DMP in a simulated 2DoF robot arm. The investigation demonstrates that the effect of the robot body dynamics needs to be taken into account during the learning process of the DMP.