Edoardo Datteri

Adaptable semi-autonomy in personal robots

Personal robotics is widely recognized as a major challenge for current robotics research. Robots assisting humans and closely interacting with them have to meet acceptability requirements which in turn leads one to reconsider the concept of autonomy in robotics. The paper presents an abstract analysis of possible levels of semi-autonomy in personal robots and illustrates a case study in which adaptable semi-autonomy is implemented and experimentally validated. Explanation modules for human-robot communication in the planning phase, and user modeling techniques, that allow the system to adapt to its users needs and preferences, are proposed as ways to achieve adaptive semi-autonomy.

Predicting the Long-Term Effects of Human-Robot Interaction: A Reflection on Responsibility in Medical Robotics

This article addresses prospective and retrospective responsibility issues connected with medical robotics. It will be suggested that extant conceptual and legal frameworks are sufficient to address and properly settle most retrospective responsibility problems arising in connection with injuries caused by robot behaviours (which will be exemplified here by reference to harms occurred in surgical interventions supported by the Da Vinci robot, reported in the scientific literature and in the press). In addition, it will be pointed out that many prospective responsibility issues connected with medical robotics are nothing but well-known robotics engineering problems in disguise, which are routinely addressed by roboticists as part of their research and development activities: for this reason they do not raise particularly novel ethical issues. In contrast with this, it will be pointed out that novel and challenging prospective responsibility issues may emerge in connection with harmful events caused by normal robot behaviours. This point will be illustrated here in connection with the rehabilitation robot Lokomat.

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.

Scientific models and ethical issues in hybrid bionic systems research

Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires reflecting on the availability of scientific models accounting for key aspects of sensorimotor coordination and plastic adaptation mechanisms in the brain.

Model testing, prediction and experimental protocols in neuroscience: a case study.

In their theoretical and experimental reflections on the capacities and behaviours of living systems, neuroscientists often formulate generalizations about the behaviour of neural circuits. These generalizations are highly idealized, as they omit reference to the myriads of conditions that could perturb the behaviour of the modelled system in real-world settings. This article analyses an experimental investigation of the behaviour of place cells in the rat hippocampus, in which highly idealized generalizations were tested by comparing predictions flowing from them with real-world experimental results. The aim of the article is to identify (1) under what conditions even single prediction failures regarding the behaviour of single cells sufficed to reject highly idealized generalizations, and (2) under what conditions prima facie counter-examples were deemed to be irrelevant to the testing of highly idealized generalizations. The results of this analysis may contribute to understanding how idealized models are tested experimentally in neuroscience and used to make reliable predictions concerning living systems in real-world settings.

The Game of Science: An Experiment in Synthetic Roboethology with Primary School Children

Educational robots may be used for purposes other than teaching computer science and robotics. In this article, we describe an educational robotics activity designed to enhance primary school childrens ability to conduct cross-disciplinary scientific inquiry, not by requiring them to build and program robots but by inviting them to work out the mechanism(s) governing preprogrammed robots.

Box-and-arrow explanations need not be more abstract than neuroscientific mechanism descriptions

The nature of the relationship between box-and-arrow (BA) explanations and neuroscientific mechanism descriptions (NMDs) is a key foundational issue for cognitive science. In this article we attempt to identify the nature of the constraints imposed by BA explanations on the formulation of NMDs. On the basis of a case study about motor control, we argue that BA explanations and NMDs both identify regularities that hold in the system, and that these regularities place constraints on the formulation of NMDs from BA analyses, and vice versa. The regularities identified in the two kinds of explanation play a crucial role in reasoning about the relationship between them, and in justifying the use of neuroscientific experimental techniques for the empirical testing of BA analyses of behavior. In addition, we make claims concerning the similarities and differences between BA analyses and NMDs. First, we argue that both types of explanation describe mechanisms. Second, we propose that they differ in terms of the theoretical vocabulary used to denote the entities and properties involved in the mechanism and engaging in regular, mutual interactions. On the contrary, the notion of abstractness, defined as omission of detail, does not help to distinguish BA analyses from NMDs: there is a sense in which BA analyses are more detailed than NMDs. In relation to this, we also focus on the nature of the extra detail included in NMDs and missing from BA analyses, arguing that such detail does not always concern how the system works. Finally, we propose reasons for doubting that BA analyses, unlike NMDs, may be considered “mechanism sketches.” We have developed these views by critically analyzing recent claims in the philosophical literature regarding the foundations of cognitive science.

Theoretical vocabularies and styles of explanation of robot behaviours in children

Abstract How do children describe and explain the behaviour of robotic systems? In this paper, some distinctions between different types of explanations, drawing from the philosophy of science literature, are proposed and exemplified by reference to an activity in which primary school children are asked to describe and explain the behaviour of a pre-programmed Braitenberg-like vehicle. The proposed distinctions are also discussed against other studies drawn from the related scientific literature. A qualitative study has provided insights to further refine the analysis described here, through the introduction of other sub-categories of explanation of robotic behaviours.

The Epistemic Value of Brain---Machine Systems for the Study of the Brain

Bionic systems, connecting biological tissues with computer or robotic devices through brain–machine interfaces, can be used in various ways to discover biological mechanisms. In this article I outline and discuss a “stimulation-connection” bionics-supported methodology for the study of the brain, and compare it with other epistemic uses of bionic systems described in the literature. This methododology differs from the “synthetic”, simulative method often followed in theoretically driven Artificial Intelligence and cognitive (neuro) science, even though it involves machine models of biological systems. I also bring the previous analysis to bear on some claims on the epistemic value of bionic technologies made in the recent philosophical literature. I believe that the methodological reflections proposed here may contribute to the piecewise understanding of the many ways bionic technologies can be deployed not only to restore lost sensory-motor functions, but also to discover brain mechanisms.

Large-scale simulations of brain mechanisms: beyond the synthetic method

In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be sharply distinguished from one another, and that correspond to conceptually different interpretations of the notion of “biological accuracy”. They include the ambition (i) to reach extremely deep levels in the mechanistic decomposition hierarchy, (ii) to simulate networks composed of extremely large numbers of neural units, (iii) to build systems able to generate rich behavioural repertoires, (iv) to simulate “complex” neuron models, (v) to implement the “best” theories available on brain structure and function. Some questions will be raised concerning the significance of each of these modelling ambitions with respect to the various roles played by simulations in the study of the brain.