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

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.

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

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  1. Dipartimento di Scienze Fisiche (DSF)Università di Napoli Federico II Guglielmo Tamburrini

  2. 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.

  3. The PRISMA Lab • 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

  4. PRISMA for ETHICBOTS • 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

  5. Ethics and technical issues • 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

  6. Other aspects • 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!

  7. Adaptive hypermedia User Interface hidden links Button selecting more or fewer details annotation: "the page has been visited" hidden links Trash for hiding or restoring links Button for the guided tour

  8. VHDL code neural network VHDL compiler Logic symbolic expressions cNSBL Formal neural network To peripheral devices inputs NSP (FPGA) Neurosymbolic compiler To symbolic interfaces Intelligent control for reactive systems

  9. 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.)

  10. 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” approaches Tamburrini, G., Datteri, E. (2005), “Machine experiments and theoretical modelling: From Cybernetics to Neuro-robotics”, Minds and Machines.

  11. Principled implementation on a robot Hypothetical explanatory model Comparison between robot and biological system performance Phenomenon to be explained Corroboration or revision of the hypothetical model Methodology of bio-robotics(and bionics)

  12. 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?

  13. Historical LandmarksNorbert Wiener: reconciling freedom and responsibility

  14. 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…”

  15. 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…

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