Guglielmo Tamburrini

Biorobotic Experiments for the Discovery of Biological Mechanisms

Robots are being extensively used for the purpose of discovering and testing empirical hypotheses about biological sensorimotor mechanisms. We examine here methodological problems that have to be addressed in order to design and perform “good” experiments with these machine models. These problems notably concern the mapping of biological mechanism descriptions into robotic mechanism descriptions; the distinction between theoretically unconstrained “implementation details” and robotic features that carry a modeling weight; the role of preliminary calibration experiments; the monitoring of experimental environments for disturbing factors that affect both modeling features and theoretically unconstrained implementation details of robots. Various assumptions that are gradually introduced in the process of setting up and performing these robotic experiments become integral parts of the background hypotheses that are needed to bring experimental observations to bear on biological mechanism descriptions.

Ethical monitoring of brain-machine interfaces: A note on personal identity and autonomy

The ethical monitoring of brain-machine interfaces (BMIs) is discussed in connection with the potential impact of BMIs on distinguishing traits of persons, changes of personal identity, and threats to personal autonomy. It is pointed out that philosophical analyses of personhood are conducive to isolating an initial thematic framework for this ethical monitoring problem, but a contextual refinement of this initial framework depends on applied ethics analyses of current BMI models and empirical case-studies. The personal autonomy-monitoring problem is approached by identifying various ways in which the inclusion of a robotic controller in the motor pathway of an output BMI may limit or jeopardize personal autonomy.

Learning robots interacting with humans: from epistemic risk to responsibility

The import of computational learning theories and techniques on the ethics of human-robot interaction is explored in the context of recent developments of personal robotics. An epistemological reflection enables one to isolate a variety of background hypotheses that are needed to achieve successful learning from experience in autonomous personal robots. The conjectural character of these background hypotheses brings out theoretical and practical limitations in our ability to predict and control the behaviour of learning robots in their interactions with humans. Responsibility ascription problems, which concern damages caused by learning robot actions, are analyzed in the light of these epistemic limitations. Finally, a broad framework is outlined for ethically motivated scientific inquiries, which aim at improving our capability to understand, anticipate, and selectively cope with harmful errors by learning robots.

Machine Experiments and Theoretical Modelling: from Cybernetic Methodology to Neuro-Robotics

Cybernetics promoted machine-supported investigations of adaptive sensorimotor behaviours observed in biological systems. This methodological approach receives renewed attention in contemporary robotics, cognitive ethology, and the cognitive neurosciences. Its distinctive features concern machine experiments, and their role in testing behavioural models and explanations flowing from them. Cybernetic explanations of behavioural events, regularities, and capacities rely on multiply realizable mechanism schemata, and strike a sensible balance between causal and unifying constraints. The multiple realizability of cybernetic mechanism schemata paves the way to principled comparisons between biological systems and machines. Various methodological issues involved in the transition from mechanism schemata to their machine instantiations are addressed here, by reference to a simple sensorimotor coordination task. These concern the proper treatment of ceteris paribus clauses in experimental settings, the significance of running experiments with correct but incomplete machine instantiations of mechanism schemata, and the advantage of operating with real machines ??? as opposed to simulated ones ??? immersed in real environments.

From motor to sensory processing in mirror neuron computational modelling

Typical patterns of hand-joint covariation arising in the context of grasping actions enable one to provide simplified descriptions of these actions in terms of small sets of hand-joint parameters. The computational model of mirror mechanisms introduced here hypothesizes that mirror neurons are crucially involved in coding and making this simplified motor information available for both action recognition and control processes. In particular, grasping action recognition processes are modeled in terms of a visuo-motor loop enabling one to make iterated use of mirror-coded motor information. In simulation experiments concerning the classification of reach-to-grasp actions, mirror-coded information was found to simplify the processing of visual inputs and to improve action recognition results with respect to recognition procedures that are solely based on visual processing. The visuo-motor loop involved in action recognition is a distinctive feature of this model which is coherent with the direct matching hypothesis. Moreover, the visuo-motor loop sets the model introduced here apart from those computational models that identify mirror neuron activity in action observation with the final outcome of computational processes unidirectionally flowing from sensory (and usually visual) to motor systems.

A note on discreteness and virtuality in analog computing

The need for physically motivated discreteness and finiteness conditions emerges in models of both analog and digital computing that are genuinely concerned with physically realizable computational processes. This is brought out by a critical examination of notional analog superTuring devices which involve physically untenable idealizations about the perfect functioning of analog apparatuses and infinite precision of physical measurements. The capability for virtual behaviour, that is, the capability of interpreting, storing, transforming, creating the code, and thereby mimicking the behaviour of (Turing) machines, is used here to introduce a new dimension in the discussion of the analog-digital watershed. In the light of recent results on the analog simulation of digital computing, we examine the role of virtuality as a discriminating factor between these two species of computing, and immerse this problem in the context of natural computing. Is virtuality instantiated in parts of the natural world other than computer technology? This broad issue is examined in connection with the computational modelling of brain and mental information processing.

Purely neural, rule-based diagnostic systems. I: Production rules

This is the first of two articles presenting an approach to rule‐based expert systems for diagnostic tasks exploiting a purely neural architecture. Here, we outline the methodological options motivating this approach, and describe a forward and backward chaining mechanism on a system of production rules. This inference engine is furnished with an informative justification module, which exploits the fact that most individual neurons get a precise semantic assignment in terms of the literals appearing in production rules. the control and synchronization functions needed to schedule these processes are carried out by a neural network, too.

Scientific Theories of Computational Systems in Model Checking

Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings between these scientific theories and computational systems in their scope are analyzed in terms of suitable specializations of the notions of model of experiment and model of data. Furthermore, the extensively mechanized character of model-based reasoning in model checking is highlighted by a comparison with proof procedures adopted by other formal methods in computer science. Finally, potential epistemic benefits flowing from the application of model checking in other areas of scientific inquiry are emphasized in the context of computer simulation studies of biological information processing.

Social Robotics and Societies of Robots

The sustainability of social robotics, like other ambitious research programs, depends on the identification of lines of inquiry that are coherent with its visionary goals while satisfying more stringent constraints of feasibility and near-term payoffs. Within these constraints, this article outlines one line of inquiry that seems especially viable: development of a society of robots operating within the physical environments of everyday human life, developing rich robot–robot social exchanges, and yet, refraining from any physical contact with human beings. To pursue this line of inquiry effectively, sustained interactions between specialized research communities in robotics are needed. Notably, suitable robotic hand design and control principles must be adopted to achieve proper robotic manipulation of objects designed for human hands that one finds in human habitats. The Pisa-IIT SoftHand project promises to meet these manipulation needs by a principled combination of sensorimotor synergies and soft robotics actuation, which aims at capturing how the biomechanical structure and neural control strategies of the human hand interact so as to simplify and solve both control and sensing problems.

Human-robot interaction and psychoanalysis

Psychological attitudes towards service and personal robots are selectively examined from the vantage point of psychoanalysis. Significant case studies include the uncanny valley effect, brain-actuated robots evoking magic mental powers, parental attitudes towards robotic children, idealizations of robotic soldiers, persecutory fantasies involving robotic components and systems. Freudian theories of narcissism, animism, infantile complexes, ego ideal, and ideal ego are brought to bear on the interpretation of these various items. The horizons of Human-robot Interaction are found to afford new and fertile grounds for psychoanalytic theorizing beyond strictly therapeutic contexts.