Hugo Gravato Marques

Body Schema in Robotics: A Review

How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.

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.

CALIPER: A universal robot simulation framework for tendon-driven robots

The development of increasingly complex robots in recent years has been characterized by an extensive use of physics-based simulations for controller design and optimization. Today, a variety of open-source and commercial simulators exist for this purpose for mobile and industrial robots. However, existing simulation engines still lack support for the emerging class of tendon-driven robots. In this paper, an innovative simulation framework for the simulation of tendon-driven robots is presented. It consists of a generic physics simulator capable of utilizing CAD robot models and a set of additional tools for simulation control, data acquisition and system investigation. The framework software architecture has been designed using component-based development principles to facilitate the framework extension and customization. Furthermore, for inter-component communication, the operating-system and programming language independent Common Object Request Broker Architecture (CORBA) [1] has been used which simplifies the integration of the framework into existing software environments.

From spontaneous motor activity to coordinated behaviour: a developmental model.

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.

Self-organization of reflexive behavior from spontaneous motor activity

In mammals, the development of reflexes is often regarded as an innate process. However, recent findings show that fetuses are endowed with favorable conditions for ontogenetic development. In this article, we hypothesize that the circuitry of at least some mammalian reflexes can be self-organized from the sensory and motor interactions brought forth in a musculoskeletal system. We focus mainly on three reflexes: the myotatic reflex, the reciprocal inhibition reflex, and the reverse myotatic reflex. To test our hypothesis, we conducted a set of experiments on a simulated musculoskeletal system using pairs of agonist and antagonist muscles. The reflex connectivity is obtained by producing spontaneous motor activity in each muscle and by correlating the resulting sensor and motor signals. Our results show that, under biologically plausible conditions, the reflex circuitry thus obtained is consistent with that identified in relation to the analogous mammalian reflexes. In addition, they show that the reflex connectivity obtained depends on the morphology of the musculoskeletal system as well as on the environment that it is embedded in.

Synthesising a Motor-Primitive Inspired Control Architecture for Redundant Compliant Robots

This paper presents a control architecture for redundant and compliant robots inspired by the theory of biological motor primitives which are theorised to be the mechanism employed by the central nervous system in tackling the problem of redundancy in motor control. In our framework, inspired by self-organisational principles, the simulated robot is first perturbed by a form of spontaneous motor activity and the resulting state trajectory is utilised to reduce the control dimensionality using proper orthogonal decomposition. Motor primitives are then computed using a method based on singular value decomposition. Controllers for generating reduced dimensional commands to reach desired equilibrium positions in Cartesian space are then presented. The proposed architecture is successfully tested on a simulation of a compliant redundant robotic pendulum platform that uses antagonistically arranged series-elastic actuation.

Self-organization of Spinal Reflexes Involving Homonymous, Antagonist and Synergistic Interactions

Recent results in spinal research are challenging the historical view that the spinal reflexes are mostly hardwired and fixed behaviours. In previous work we have shown that three of the simplest spinal reflexes could be self-organised in an agonist-antagonist pair of muscles. The simplicity of these reflexes is given from the fact that they entail at most one interneuron mediating the connectivity between afferent inputs and efferent outputs. These reflexes are: the Myotatic, the Reciprocal Inibition and the Reverse Myotatic reflexes. In this paper we apply our framework to a simulated 2D leg model actuated by six muscles (mono- and bi-articular). Our results show that the framework is successful in learning most of the spinal reflex circuitry as well as the corresponding behaviour in the more complicated muscle arrangement.

Unsupervised Learning of a Reduced Dimensional Controller for a Tendon Driven Robot Platform

In this paper we present a developmental framework to carry out goal-oriented learning in a low-dimensional space. The framework uses two stages of learning: one to synthesise a set of motor synergies and reduce the dimensionality of the control space in an unsupervised manner, and another to carry out supervised learning in the reduced control space. We test our framework in a reaching task carried out on a (real) tendon-driven robot actuated by four artificial muscles. Our results show that the robot is capable of learning to reach using a reduced control space using no prior information about its body apart from that inherent to the unsupervised and supervised learning rules.

FUNCTIONAL EMBODIED IMAGINATION AND EPISODIC MEMORY

The phenomenon of episodic memory has been studied for over 30 years, but it is only recently that its constructive nature has been shown to be closely linked to the processes underpinning imagination. This paper builds on recent work by the authors in developing architectures for a form of imagination suitable for use in artifacts, and considers how these architectures might be extended to provide a form of episodic memory.