Why Robots may need to be self-‐aware, before we can really trust them - Alan Winfield.

Why Robots may need to be self-‐aware, before we can really trust them

Alan FT Winfield Bristol Robo=cs Laboratory

Awareness Summer School, Lucca 26 June 2013

Outline

•  The safety problem •  The central proposi=on of this talk •  Introducing Internal Models in robo=cs •  A generic Internal Modelling architecture, for safety

–  worked example: a scenario with safety hazards •  Towards an ethical robot

–  worked example: a hazardous scenario with a human and a robot

•  The major challenges •  How self-‐aware would the robot be? •  A hint of neuroscien=fic plausibility

The safety problem

•  For any engineered system to be trusted, it must be safe – We already have many examples of complex engineered systems that are trusted; passenger airliners, for instance

– These systems are trusted because they are designed, built, verified and operated to very stringent design and safety standards

– The same will need to apply to autonomous systems

The safety problem

•  The problem of safe autonomous systems in unstructured or unpredictable environments, i.e. –  robots designed to share human workspaces and physically interact with humans must be safe,

–  yet guaranteeing safe behaviour is extremely difficult because the robot’s human-‐centred working environment is, by defini5on, unpredictable

–  it becomes even more difficult if the robot is also capable of learning or adapta5on

The proposi=on In unknown or unpredictable environments, safety cannot be achieved without self-‐awareness

What is an internal model?

•  It is an internal mechanism for represen=ng both the system itself and its environment – example: a robot with a simula5on of itself and its currently perceived environment, inside itself

•  The mechanism might be centralized, distributed, or emergent

“..an internal model allows a system to look ahead to the future consequences of current ac=ons, without actually commiYng itself to those ac=ons” John Holland (1992), Complex Adap=ve Systems, Daedalus.

Using internal models

•  Internal models can provide a minimal level of func5onal self-‐awareness – sufficient to allow complex systems to ask what-‐if ques=ons about the consequences of their next possible ac=ons, for safety

•  Following Dennea an internal model can generate and test what-‐if hypotheses: –  what if I carry out action x..?!–  of several possible next actions xi, which should I choose?!

Dennea’s Tower of Generate and Test

Darwinian Creatures

Skinnerian Creatures

Popperian Creatures

Dennea, D. (1995). Darwin’s Dangerous Idea, London, Penguin.

Natural Selec=on

Individual (Reinforcement) Learning

Internal Modelling

Examples 1 •  A robot using self-‐simula=on to plan a safe route with incomplete knowledge

Vaughan, R. T. and Zuluaga, M. (2006). Use your illusion: Sensorimotor self-‐ simula=on allows complex agents to plan with incomplete self-‐knowledge, in Proceedings of the Interna=onal Conference on Simula=on of Adap=ve Behaviour (SAB), pp. 298–309.

Examples 2

•  A robot with an internal model that can learn how to control itself

Bongard, J., Zykov, V., Lipson, H. (2006) Resilient machines through con=nuous self-‐modeling. Science, 314: 1118-‐1121.

Examples 3

•  ECCE-‐Robot – A robot with a complex body uses an internal model as a ‘func=onal imagina=on’

Marques, H. and Holland, O. (2009). Architectures for func=onal imagina=on, Neurocompu=ng 72, 4-‐6, pp. 743–759.

Diamond, A., Knight, R., Devereux, D. and Holland, O. (2012). Anthropomime=c robots: Concept, construc=on and modelling, Interna=onal Journal of Advanced Robo=c Systems 9, pp. 1–14.

Examples 4

•  A distributed system in which each robot has an internal model of itself and the whole system –  Robot controllers and the internal simulator are co-‐evolved

O’Dowd P, Winfield A and Studley M (2011), The Distributed Co-‐Evolu=on of an Embodied Simulator and Controller for Swarm Robot Behaviours, in Proc IEEE/RSJ Interna=onal Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, September 2011.

A Generic IM Architecture for Safety

Internal Model Evaluates the

consequences of each possible next ac=on

Sense data

Actuator demands

The loop of generate and test

The IM is ini=alized to match the current real situa=on

Robot Controller The IM

moderates ac=on-‐selec=on in the controller

Copyright © Alan Winfield 2013

A Generic IM Architecture for Safety Sense data

Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model

Consequence Evaluator

Object Tracker -‐ Localiser


N-‐tuple of all possible ac=ons (a1, a2, a3, a4)


Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model






Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model



S-‐tuple of safe ac=ons (a3, a4)



Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model



S-‐tuple of safe ac=ons (a3, a4)



A scenario with safety hazards Consider a robot that has four possible next ac=ons: 1.  turn leq 2.  move ahead 3.  turn right 4.  stand s=ll Hole

Robot

Wall


A scenario with safety hazards Consider a robot that has four possible next ac=ons: 1.  turn leq 2.  move ahead 3.  turn right 4.  stand s=ll

Hole

Wall

Robot ac(on

Posi(on change

Robot outcome

Consequence

Ahead leq 5 cm Collision Robot collides with wall

Ahead 10 cm Collision Robot falls into hole

Ahead right 20 cm No-‐collision Robot safe

Stand s=ll 0 cm No-‐collision Robot safe


Towards an ethical robot

Which robot ac=on would lead to the least harm to the human?


Towards an ethical robot

Which robot ac=on would lead to the least harm to the human?

Robot ac(on

Robot outcome

Human outcome

Consequence

Ahead leq 0 10 Robot safe; human falls into hole

Ahead 10 10 Both robot and human fall into hole

Ahead right 4 4 Robot collides with human

Stand s=ll 0 10 Robot safe; human falls into hole

Outcome scale 0:10, equivalent to Completely safe: Very dangerous


Combining safety and ethical rules

IF for all robot actions, the human is equally safe!THEN (* default safe actions *)!!output s-tuple of safe actions!

ELSE (* ethical actions *)! !output s-tuple of actions for least unsafe human

outcomes!

Consider Asimov’s 1st and 3rd laws of robo=cs: (1)  A robot may not injure a human being or, through inac=on, allow a human

being to come to harm, (3)  A robot must protect its own existence as long as such protec=on does not

conflict with the First (or Second) Laws

Isaac Asimov, I, ROBOT, 1950


Extending into Adap=vity Sense data

Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model



Learned/adap=ve behaviours


Extending into Adap=vity Sense data

Actuator demands


Robot Controller

Robot Controller

Robot Model

World Model



Learned/adap=ve behaviours


Challenges and open ques=ons

•  Fidelity: to model both the system and its environment with sufficient fidelity;

•  To connect the IM with the system’s real sensors and actuators (or equivalent);

•  Timing and data flows: to synchronize the internal model with both changing perceptual data, and efferent actuator data;

•  Valida5on, i.e. of the consequence rules.

Major challenges: performance

•  Example – imagine placing this Webots simula=on inside each NAO robot:

Note the simulated robot’s eye view of it’s world

A science of simula=on: the CoSMoS approach

The Complex Systems Modelling and Simula=on (CoSMoS) process, from Susan Stepney, et al, Engineering Simula=ons as Scien=fic Instruments — a paaern language, Springer, in prepara=on.

The CoSMoS Process Version 0.1: A Process for the Modelling and Simula=on of Complex Systems, Paul S. Andrews, et al, Dept of Computer Science, University of York, Number YCS-‐2010-‐453

Major challenges: =ming

•  When and how oqen do we need to ini=ate the generate-‐and-‐test-‐loop (IM cycle)? – Maybe when the object tracker senses a nearby object star=ng to move..?

•  How far ahead should the IM simulate – Let us call this =me ts. if ts is too short the IM will not encounter the hazard; too long will slow down the robot.

–  Ideally ts and its upper limit should be adap=ve.

How self-‐aware would this robot be?

•  The robot would not pass the mirror test – Haikkonen (2007), Reflec=ons of consciousness

•  However, I argue this robot would be minimally but sufficiently self-‐aware to merit the label – But this would have to be demonstrated by the robot behaving in interes5ng ways, that were not pre-‐programmed, in response to novel situa5ons

– Valida=ng any claims to self-‐awareness would be very challenging

Some neuroscien=fic plausibility?

•  Libet’s famous experimental result showed that ini=a=on of ac=on occurs before the conscious decision to make take that ac=on –  Libet, B (1985), Unconscious cerebral Ini=a=ve and the role of

conscious will in voluntary ac=on, Behavioral and Brain Science, 8, 529-‐539.

•  Although controversial there appears to be a growing body of opinion toward consciousness as a mechanism for vetoing ac=ons –  Libet coined the term: free won’t

In conclusion •  I strongly suspect that self-‐awareness via internal models might prove to be the only way to guarantee safety in robots, and by extension autonomous systems, in unknown and unpredictable environments – and just maybe provide ethical behaviours too

Thank you!

Reference for the work of this talk: Winfield AFT, Robots with Internal Models: A Route to Self-‐Aware and Hence Safer Robots, accepted for The Computer AJer Me, eds. Jeremy Pia and Julia Schaumeier, Imperial College Press, 2013.

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Why Robots may need to be self-‐aware, before we can really trust them - Alan Winfield.

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