Dipartimento di Scienze Fisiche (DSF)Università di Napoli Federico II

Guglielmo Tamburrini

Key research personnel Bruno Siciliano is professor of Control and Robotics,

and Director of the PRISMA Lab at University of Naples Federico II. President Elect of IEEE RAS.

Ernesto Burattini is professor of Computer Science at the University of Naples Federico II. His research interests include Knowledge-based Systems, Multimedia Intelligent Systems, Neuro-Symbolic integration, and Intelligent Control for Robots.

Giuseppe Trautteur is professor of Computer Science at the University of Naples Federico II. His research interests concern cognitive science and consciousness studies.

Luigi Villani is associate professor of Control and Robotics at the University of Naples Federico II.

Research areas redundant and cooperative manipulators impedance and force control visual tracking and servoing lightweight flexible arms space robots human-robot interaction service robotics

People Bruno Siciliano Luigi Villani Vincenzo Lippiello Agostino De Santis

The PRISMA Lab

Robots leave factories to meet humans in their every-day domains: optimality criteria change!

Design for Physical Human-Robot interaction (pHRI) must take into account new keywords

safety dependability

Cognitive Science and Ethics help to set the priorities: robots for anthropic domains must guarantee a different concept of safety and dependability with respect to industrial robots in structured environments, to avoid damages to humans

PRISMA for ETHICBOTS

Purely cognitive aspects (considering robots as “living creatures”) vs. more technical aspects

There are important ethical issues in the design of “slave” robots for physical interaction

which is the maximum acceptable level of autonomy of a robot interacting with humans?

control architecture and unpredictable behaviours

responsibility of the designer

minimizing the risks for robots interacting with humans heavy moving parts

sensory data reliability

unpredictable behaviours

Ethics and technical issues

Improvements for safety and dependability can be more or less visible (soft covering/passive compliance vs. active impedance/force control) passive-safety related facilities help

perceiving reliability active control is not clearly visible:

control is hidden (people must be informed about the presence of ABS and ESP in cars), but it adds dependability!

Other aspects

annotation: "the page has been visited"

annotation: "the page has been visited"

hidden linkshidden links

Trash for hiding or restoring links

Trash for hiding or restoring links

Button selecting more or fewer details

Button selecting more or fewer details

User InterfaceUser Interface

Button for the guided tour

Button for the guided tour

hidden linkshidden links

Adaptive hypermedia

NSP(FPGA)

inputs

To symbolic interfaces

To peripheral devices

Intelligent control for reactive systems

Logic symbolic expressions

cNSBL

Neurosymbolic compiler

Formal neural network

VHDL codeneural network

VHDL compiler

Philosophy of AI and Robotics

Machines and scientific explanation of adaptive behaviours

Machine learning and the epistemological problem of induction

Historical landmarks (Norbert Wiener, Alan Turing, etc.)

Machines in scientific explanation

What is the role, if any, of machines in providing responsibly supported

explanations of adaptive and intelligent behaviours?

Scientific rationality as opposed to “Turing-test” approachesTamburrini, G., Datteri, E. (2005), “Machine experiments and theoretical

modelling: From Cybernetics to Neuro-robotics”, Minds and Machines.

Phenom

enon to

be e

xpla

ined

Phenom

enon to

be e

xpla

ined

I

O

I

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Hypothetical explanatory modelHypothetical explanatory model

Prin

ciple

d im

ple

menta

tion o

n a

robot

Prin

ciple

d im

ple

menta

tion o

n a

robot

Corroboration or revision of the hypothetical model

Corroboration or revision of the hypothetical model

Comparison between robot and biological system performanceComparison between robot and biological system performance

Methodology of bio-robotics(and bionics)

Machine Learning and Induction

Learning from examples Inductive hypothesis (“Hume’s problem”):

Any learning hypothesis found to approximate

the target function well over a sufficiently large

set of training examples will also approximate

the target function well over unobserved

examples.

Is this inductive hypothesis justified?

Historical Landmarks

Norbert Wiener: reconciling freedom and responsibility

FreedomN. Wiener: God & Golem

“As long as automata can be made,

whether in the metal or merely in

principle, the study of their making and

their theory is a legitimate phase of

human curiosity, and human

intelligence is stultified when man sets

fixed bounds to his curiosity…”

N. Wiener, “Some moral and technical consequences of automation”, Science, May 1960

It is quite in the cards that learning machines will be used to program the pushing of the button in a new push-button war…the programming of such a learning machine would have to be based on some sort of war game... Here, however, if the rules for victory in a war game do not correspond to what we actually wish… such a machine may produce a policy which would win a nominal victory on points at the cost of every interest we have at heart…