1. EASy 5 Dec 2004HART 2004, Fukui1 HART 2004 Time and Motion Studies: The Dynamics of Cognition, Computation and Humanoid Walking Inman Harvey, Eric Vaughan, Ezequiel Di Paolo Evolutionary and Adaptive Systems Group EASy, Dept. of Informatics University of Sussex inmanh@cogs.susx.ac.uk

2. EASy 5 Dec 2004HART 2004, Fukui2

3. EASy 5 Dec 2004HART 2004, Fukui3 HOAP-2, Fujitsu

4. EASy 5 Dec 2004HART 2004, Fukui4 What are the difficulties? Why is walking so easy for us, and so difficult for robots? We suggest:- Commercial humanoid design has tended to ignore the natural dynamics of a mechanical system, for at least two reasons. (1)The conceptual framework of traditional A.I. (2)The background of industrial robot designers

5. EASy 5 Dec 2004HART 2004, Fukui5 Instead, we should be … … … … exploiting the natural dynamics of mechanical limbs in ways comparable to those of animals and humans. We should be producing designs that are not constrained by the shackles of GIFAI (Good Old Fashioned AI), nor by the constraints of conventional engineering design philosophy. The Dynamical Systems approach, with Evolutionary Robotics

6. EASy 5 Dec 2004HART 2004, Fukui6 The Glider Analogy … Compare the problems of designing humanoid walking robots with the problems that the Wright brothers faced in aiming towards the first powered flight on 17th Dec 1903. They started by understanding un-powered gliding flight, before then adding control and then power.

7. EASy 5 Dec 2004HART 2004, Fukui7 … applied to humanoid walking Passive Dynamic Walking is the equivalent of the glider Then we can progressively add Control and Power

8. EASy 5 Dec 2004HART 2004, Fukui8 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

9. EASy 5 Dec 2004HART 2004, Fukui9 Automata have a rich history Hero of Alexandria described working models of animals and humans, using hydraulics and pneumatics, some 2000 years ago

10. EASy 5 Dec 2004HART 2004, Fukui10 Clockwork technology From the 14 th Century on, clockwork allowed more sophisticated automata 18 th C, Jaquet-Droz

11. EASy 5 Dec 2004HART 2004, Fukui11 Clockwork computing In the 1820s in London, Babbage used clockwork technology to design the Difference Engine, and then the Analytical Engine … … the world’s first universal digital computer

12. EASy 5 Dec 2004HART 2004, Fukui12 KarakuriKarakuri Hanzo Yorinao Hosokawa was a Master of making mechanical puppets in the 18 th C.

13. EASy 5 Dec 2004HART 2004, Fukui13 Tanaka Hisashige Established a Hall of Automata in Kyoto in the 19 th C. (child with bow and arrow) He went on to build he first steam locomotive in Japan, and contributed to the industrialisation of Japan.

14. EASy 5 Dec 2004HART 2004, Fukui14 From Karakuri to Asimo Walking karakuri inspired humanoid robots, especially in Japan. From Waseda University in the 1960s, to Honda’s Asimo in the 1990s.

15. EASy 5 Dec 2004HART 2004, Fukui15 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

16. EASy 5 Dec 2004HART 2004, Fukui16 Constraints arising from this history The design of such humanoids has been heavily influenced by this history. 1.It has been assumed that the trajectories of limbs must be pre-planned through computations 2.Designers have tended to use stiff actuators and materials, so as to maintain close control on these pre- calculated positions

17. EASy 5 Dec 2004HART 2004, Fukui17 Computing and GOFAI Babbage’s computers in the 19 th C were based on clockwork, and 20 th C computers as invented by Turing and Von Neumann proceed by the ticks of a clock. The dynamics of time, the sun, and a sundial are continuous … but a clock and a computer go in a discrete sequence of ticks

18. EASy 5 Dec 2004HART 2004, Fukui18 GOFAI assumptions So there is a natural tendency to analyse even a dynamical process such as biped walking as a succession of moves between instantaneous frozen positions. The dynamic has been reduced to transitions between static snapshots. This is one cause of the rather unnatural underlying principles of many commercial bipeds today.

19. EASy 5 Dec 2004HART 2004, Fukui19 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

20. EASy 5 Dec 2004HART 2004, Fukui20 The Dynamical Systems approach In contrast to GOFAI:- The limbs of an animal, a human, or a robot – and their nervous systems, real or artificial – are physical systems with positions and values acting on each other smoothly in continuous real time. Walking has a natural dynamics arising from the swing of limbs under gravity – synthesis an artificial system that respects such natural dynamics

21. EASy 5 Dec 2004HART 2004, Fukui21 Passive Dynamic Walking With upper and lower legs, and un-powered thigh and knee joints, a biped can walk down a slope with no control system … in simulation …

22. EASy 5 Dec 2004HART 2004, Fukui22 … or in Reality Collins, Cornell.

23. EASy 5 Dec 2004HART 2004, Fukui23 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

24. EASy 5 Dec 2004HART 2004, Fukui24 Evolutionary Robotics Rather than forcing limbs to follow a pre-planned trajectory, we want to design in the appropriate natural dynamics. When we add an artificial nervous system, we also want to design in the appropriate dynamics for this, coupled to actuators and sensors. We are designing dynamical systems, not calculating trajectories – this can be difficult. Animals and humans are designed through Darwinian evolution – we can use Artificial Evolution

25. EASy 5 Dec 2004HART 2004, Fukui25 Artificial Evolution 1.Set up a mapping from strings of “Artificial DNA” to designs of robot bodies and their “nervous systems” 2.Start with a random population of DNA-strings – generating random and probably useless designs 3.Test each design, and pick out the “fitter” ones 4.Breed from the fitter ones – recombine and mutate their DNA to produce offspring 5.This makes a new generation – return to (2)

26. EASy 5 Dec 2004HART 2004, Fukui26 Evolutionary Robotics The DNA will encode 1.The lengths, centres of mass, angles, spring constants, range of motor forces of the robot body 2.The connectivities, weights, biases and time parameters of a robot nervous systems

27. EASy 5 Dec 2004HART 2004, Fukui27 TestingTesting Typically the designs are tested in a physics simulator such as ODE. Noise or uncertainty is added to the dimensions of the robot, to the physical forces – and where there is a control system, to sensors, actuators and the “nervous system”. The simulations are computational – but the designs being simulated are not. They are real-time Dynamical Systems. An “Envelope of Noise” can assist in transferring from simulation to a real physical robot (Jakobi et al)

28. EASy 5 Dec 2004HART 2004, Fukui28 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

29. EASy 5 Dec 2004HART 2004, Fukui29 GOFAI Humanoid Design Because of the historical constraints on their approach, GOFAI designers have used stiff actuators and materials. High impedance systems, so that unplanned variations are resisted. The Honda robot uses a version of ZMP, Zero-Moment Point Control, that requires it to accurately obey precisely calculated trajectories – only modified by force sensors in the ankles – a high impedance solution.

30. EASy 5 Dec 2004HART 2004, Fukui30 ZMPZMP Engineers find it much easier to measure positions rather than forces. Hence the tendency towards stiff, high-impedance solutions. If the leg of a biped is allowed to straighten up at the knee, then ZMP calculations have a singularity with no sensible solution – hence the characteristic bent knees.

31. EASy 5 Dec 2004HART 2004, Fukui31 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

32. EASy 5 Dec 2004HART 2004, Fukui32 Remember the Glider Analogy Our approach, following the Wright brothers, is to perfect a glider first, then add power and control. Can the PDW scale up? How about with 4 knees?

33. EASy 5 Dec 2004HART 2004, Fukui33 10 degrees of freedom version Damped ankle and hip springs

34. EASy 5 Dec 2004HART 2004, Fukui34 Passive Dynamic Walking No power except potential energy, no control system

35. EASy 5 Dec 2004HART 2004, Fukui35 Plan of Talk 1.Historical roots of Humanoid robotics 2.Models of Cognition – GOFAI 3.Models of Cognition – Dynamical Systems approach 4.Designing Dynamical Systems – Evolutionary Robotics 5.GOFAI Humanoid walking – ZMP methods 6.DS Humanoid Walking – PDW Passive Dynamic Walkers 7.Adding Control and Power to PDW

36. EASy 5 Dec 2004HART 2004, Fukui36 Adding Control and Power In the Passive versions, there are no sensors, no motors no control system. Add force sensors, accelerometers, gyroscopes, rotation sensors. Add actuators connected to springs – low impedance. Add continuous time recurrent neural networks – and evolve them.

37. EASy 5 Dec 2004HART 2004, Fukui37 Some examples Powered walking on the flat Balancing on a moving platform Walking back and forward (2D version)

38. EASy 5 Dec 2004HART 2004, Fukui38 ConclusionsConclusions