Knowledge Engineering in robotics

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1 Knowledge Engineering in robotics Herman Bruyninckx K.U.Leuven, Belgium BRICS, Rosetta, eurobotics Västerås, Sweden April 8, 2011 Herman Bruyninckx, Knowledge Engineering in robotics 1

2 BRICS, Rosetta, eurobotics BRICS: Rosetta: eurobotics: Herman Bruyninckx, Knowledge Engineering in robotics 2

3 BRICS, Rosetta, eurobotics BRICS: software engineering for complex robotic systems how to do that in (Eclipse, MDE) tool support = lots of knowledge engineering Rosetta: eurobotics: Herman Bruyninckx, Knowledge Engineering in robotics 2

4 BRICS, Rosetta, eurobotics BRICS: software engineering for complex robotic systems how to do that in (Eclipse, MDE) tool support = lots of knowledge engineering Rosetta: intelligent skills for force-controlled robotic assembly skill to be described at several levels of abstraction = lots of knowledge engineering eurobotics: Herman Bruyninckx, Knowledge Engineering in robotics 2

5 BRICS, Rosetta, eurobotics BRICS: software engineering for complex robotic systems how to do that in (Eclipse, MDE) tool support = lots of knowledge engineering Rosetta: intelligent skills for force-controlled robotic assembly skill to be described at several levels of abstraction = lots of knowledge engineering eurobotics: semantic web for robotics portal need for open content robotics ontology Herman Bruyninckx, Knowledge Engineering in robotics 2

6 Examples in my research Herman Bruyninckx, Knowledge Engineering in robotics 3

7 Knowledge representation Knowledge needed is of various types: robot motion controllers geometry of objects + scene graph sensor capabilities & data interpretation (partial) ordering of actions in task common sense + physical laws relationships robot actions effects... Herman Bruyninckx, Knowledge Engineering in robotics 4

8 Knowledge representation Knowledge needed is of various types: robot motion controllers geometry of objects + scene graph sensor capabilities & data interpretation (partial) ordering of actions in task common sense + physical laws relationships robot actions effects... Representation of knowledge: (hyper)graphs (Topic Maps, RDF,... ) rules (logic, OWL-x,... ) Herman Bruyninckx, Knowledge Engineering in robotics 4

9 Knowledge representation Knowledge needed is of various types: robot motion controllers geometry of objects + scene graph sensor capabilities & data interpretation (partial) ordering of actions in task common sense + physical laws relationships robot actions effects... Representation of knowledge: (hyper)graphs (Topic Maps, RDF,... ) rules (logic, OWL-x,... ) How to integrate them...? Herman Bruyninckx, Knowledge Engineering in robotics 4

10 Types of ontologies object ontology domain/system ontology profile ontology Herman Bruyninckx, Knowledge Engineering in robotics 5

11 Types of ontologies object ontology what knowledge do our robots need to become intelligent domain/system ontology profile ontology Herman Bruyninckx, Knowledge Engineering in robotics 5

12 Types of ontologies object ontology what knowledge do our robots need to become intelligent domain/system ontology what is Field robotics? Or Assembly robotics? Reference: Hallam & Bruyninckx, An ontology of robotics science, First European Robotics Symposium, profile ontology Herman Bruyninckx, Knowledge Engineering in robotics 5

13 Types of ontologies object ontology what knowledge do our robots need to become intelligent domain/system ontology what is Field robotics? Or Assembly robotics? Reference: Hallam & Bruyninckx, An ontology of robotics science, First European Robotics Symposium, profile ontology what are the competences/expertise of a researcher? Herman Bruyninckx, Knowledge Engineering in robotics 5

14 MDE s M0 M3 & ontology M3 M2 meta model (DSL) metametamodel instance of instance of meta model (DSL) DSL Designer M1 domain model domain model domain model DSL User instance of M0 Real-world systems Herman Bruyninckx, Knowledge Engineering in robotics 6

15 MDE s M0 M3 & ontology M3 M2 meta model (DSL) metametamodel instance of instance of meta model (DSL) DSL Designer M1 domain model domain model domain model DSL User instance of M0 Real-world systems M0 M3 is ontology (not other way around!) Herman Bruyninckx, Knowledge Engineering in robotics 6

16 MDE s M0 M3 & ontology M3 M2 meta model (DSL) metametamodel instance of instance of meta model (DSL) DSL Designer M1 domain model domain model domain model DSL User instance of M0 Real-world systems M0 M3 is ontology (not other way around!) Claim: MDE s Domain Specific Language concept is pragmatic way to start robotics objects ontology, in particular for action representation Herman Bruyninckx, Knowledge Engineering in robotics 6

17 DSL for assembly case Discrete behaviour: FSM move_up = apply(tff_motions.move_up, {zt=-0.3}) end move_down = apply(tff_motions.move_down, {zt=0.1}) end align = apply(tff_motions.push_down, {zt=10}) end slide_x = apply(tff_motions.compliant_slide_x, {xt=0.2, zt=1}) end trans:new{ src="initial", tgt="move_down" }, trans:new{ src="move_up", tgt="move_down", guard = return get_total_distance() > 0.2 end }, trans:new{ src="align", tgt="slide_x", guard = return get_move_duration() > 2 end }, Herman Bruyninckx, Knowledge Engineering in robotics 7

18 DSL for assembly case Continuous behaviour: control move_down = xt = tff.axis_spec:new { value=0, type= velocity } yt = tff.axis_spec:new { value=0, type= velocity } zt = tff.axis_spec:new { value=0.01, type= velocity }) compliant_slide = xt = tff.axis_spec:new { value=0, type= force } yt = tff.axis_spec:new { value=-0.03, type= velocity zt = tff.axis_spec:new { value=1, type= force } Herman Bruyninckx, Knowledge Engineering in robotics 8

19 Robot systems: M2 M3 model CompositeComponent Component1 Communication3 Component3 (computation) Communication1 Communication2 Component2 (computation) Communication4 (computation) Structural model + Communication + Coordination Herman Bruyninckx, Knowledge Engineering in robotics 9

20 Robot systems: M2 M3 model CompositeComponent Component1 Communication3 Component3 (computation) Communication1 Communication2 Component2 (computation) Communication4 (computation) Structural model + Communication + Coordination Components: control, learning, planning,... M0 M1 framework DSLs: Orocos + ROS Herman Bruyninckx, Knowledge Engineering in robotics 9

21 3D perception stack: M1 M2 model obj1 task obj2 fea1 fea2 fea3 task obj fea robot motion (predicted object motion) 3D object motion model (predicted feature motion) feature motion in sensor space sen1 sen2 sen (focus of attention) Herman Bruyninckx, Knowledge Engineering in robotics 10

22 3D perception stack: M1 M2 model obj1 task obj2 fea1 fea2 fea3 task obj fea robot motion (predicted object motion) 3D object motion model (predicted feature motion) feature motion in sensor space sen1 sen2 sen (focus of attention) Bayesian probability excellent candidate for DSL! Herman Bruyninckx, Knowledge Engineering in robotics 10

23 Skills: M2 M3 model Task Skill Motion Herman Bruyninckx, Knowledge Engineering in robotics 11

24 Skills: M2 M3 model Task Skill Motion The Skill is a probabilistic state machine: state machine encodes causality/(partial) ordering events couple the symbolic and continuous domains. Herman Bruyninckx, Knowledge Engineering in robotics 11

25 Skills: M2 M3 model (2) PIM "platform-independent model" platform constraints "platform-specific model" PSM To add knowledge on: robot, controller, sensor, learning algorithm,... Herman Bruyninckx, Knowledge Engineering in robotics 12

26 Skills: M2 M3 model (3) Task platform constraints Skill Motion The platform constraints define parameters in the FSM behaviour. Herman Bruyninckx, Knowledge Engineering in robotics 13

27 Skills: M2 M3 model (4) Task knowledge transformations platform constraints Skill Motion The Skill states are instantiations of logic symbols, and run continuous time/space control & sensing algorithms. Herman Bruyninckx, Knowledge Engineering in robotics 14

28 Conclusions major challenge: not so much the amount but the variation of different types of knowledge Herman Bruyninckx, Knowledge Engineering in robotics 15

29 Conclusions major challenge: not so much the amount but the variation of different types of knowledge our research: proposes probabilistic finite state machine(s) as key for integration: Herman Bruyninckx, Knowledge Engineering in robotics 15

30 Conclusions major challenge: not so much the amount but the variation of different types of knowledge our research: proposes probabilistic finite state machine(s) as key for integration: focus about what knowledge and learning to use, at each moment in a robot s task grounding & closing the world: obvious lends itself very well for DSL representation Need for open content publicly available ontology server! Herman Bruyninckx, Knowledge Engineering in robotics 15

31 Conclusions major challenge: not so much the amount but the variation of different types of knowledge our research: proposes probabilistic finite state machine(s) as key for integration: focus about what knowledge and learning to use, at each moment in a robot s task grounding & closing the world: obvious lends itself very well for DSL representation Need for open content publicly available ontology server! multi-project cooperation can start now! Herman Bruyninckx, Knowledge Engineering in robotics 15

32 Conclusions major challenge: not so much the amount but the variation of different types of knowledge our research: proposes probabilistic finite state machine(s) as key for integration: focus about what knowledge and learning to use, at each moment in a robot s task grounding & closing the world: obvious lends itself very well for DSL representation Need for open content publicly available ontology server! multi-project cooperation can start now! what license shall we use...? (Creative Commons Share alike!?) Herman Bruyninckx, Knowledge Engineering in robotics 15

Service Robots in an Intelligent House

Service Robots in an Intelligent House Jesus Savage Bio-Robotics Laboratory biorobotics.fi-p.unam.mx School of Engineering Autonomous National University of Mexico UNAM 2017 OUTLINE Introduction A System