Sensory Anticipation in Bio-Robotics and Neurosciences

1. L’anticipazione in bio-robotica e nelle neuroscienze Edoardo Datteri ARTS Lab, Scuola Superiore Sant’Anna Dipartimento di Filosofia, Università di Pisa edoardo@mail-arts.sssup.it

2. Schema • Schemi di coordinazione sensomotoria basati sull’anticipazione sensoriale o motoria • L’anticipazione in robotica • Lo schema di coordinazione sensomotoria basato sulla percezione attesa (ARTS Lab + Dip. Filosofia) • Brainstorming

3. anticipazione sensoriale anticipazione motoria L’anticipazione • Capacità di generare previsioni su quello che sarà l’ingresso sensoriale del sistema in istanti futuri (anticipazione sensoriale) • Capacità di eseguire azioni preparatorie (anticipazione motoria)

4. Il problema • Formulare modelli adatti a spiegare e a replicare la coordinazione sensomotoria esibita da sistemi biologici in ambienti reali • Fattore chiave: velocità della reazione • Navigazione • Presa • Manipolazione

5. Typical sequential feedback-based perception-action scheme Motor command Desired state controller robot Action Sensed state feedback

6. R. Arkin, Behavior-based robotics, MIT Press, 1998 Behaviour-based architecture layers are parallel and asynchronous sensors actuator The motor response ri of behaviour i at time t is where st is the state of the sensor associated with behaviour i at time t, is a real-valued weight, and

7. Delays in human nervous system “In motor control delays arise in sensory transduction, central processing, and in the motor output. Sensor transduction latencies are most noticeable in the visual system where the retina introduces a delay of 30-60 ms, but sensory conduction delays can also be appreciable. Central delays are also present due to such ill-defined events such as neural computation, decision making and the bottlenecks in processing command. Delays in the motor output result from motorneuronal axonal conduction delays, muscle exictation-contraction delays, and phase lags due to the intertia of the system. These delays combine to give an unavoidable feedback delay within the negative feedback control loop, and can lie between about 30 ms for a spinal reflex up to 200-300 ms for a visually guided response.” R.C. Miall, D.J. Weir, D.M. Wolpert, J.F. Stein, “Is the cerebellum a Smith predictor?”, Journal of Motor Behavior, vol. 25, no. 3, pp. 203-216, 1993 “Fast and coordinated arm movements cannot be executedunder pure feedback control because biological feedbackloops are both too slow and have small gains” M. Kawato, Internal models for motor control andtrajectory planning. Current Opinion in Neurobiology, 9, 718-727(1999). Elsevier Science Ltd.

8. calibration of grip and load force in manipulation visuomotor coordination in manipulation Sensory anticipation proposed by Johansson “Because of the long time delays with feedback control the swift coordinationof fingertip forces during self-paced everyday manipulation of ordinary‘passive’ objects must be explained by other mechanisms. Indeed, the brainrelies on feedforward control mechanisms and takes advantage of the stableand predictable physical properties of these objects by parametricallyadapting force motor commands to the relevant physical properties of thetarget object.” corrections are generated when expected sensory inputs don’t match the actual ones expected sensory event

9. Lo schema di Johansson

10. Anticipation in robotics and bio-robotics • Bio-robotics • Broad hypotheses on intelligence, reasoning, adaptiveness… • Specific hypotheses on anticipation in adaptive behaviours (Webb, 2004) • Robotics • Motor anticipation: • to achieve more efficient manipulation and interaction with humans • Sensory anticipation: • to obtain faster sensorimotor coordination loops, in case of complex sensory data • to obtain more accurate sensorimotor coordination loops, in case of noisy sensory data

11. R.C. Miall and D.M. Wolpert, Forward Models for Physiological Motor Control. Neural Networks, vol. 9, no. 8, pp. 1265-1279, 1996 Forward models • Potential uses: • Cancelling sensory re-afference • Distal supervised learning • State estimation (Kalman filter) • Internal feedback to overcome time delays

12. Cancelling sensory reafference

13. Distal Supervised Learning

14. State estimation forward model

15. when the actual sensory feedback comes, it is compared with the expected one to check if the inner cycle works properly in the inner loop, the sensory feedback is replaced by an estimated one, generated on the basis of internal models Internal feedback to overcome time delays The Smith Predictor

16. La percezione è un processo “ipotesi-test-revisione” Architettura MASIM (Gross et al., 1999)

17. La percezione è un processo “ipotesi-test-revisione” Il processo di generazione di previsioni sensoriali può guidare elaborazioni dei dati sensoriali in ingresso al sistema generatore di previsioni sensoriali previsione sensoriale letture sensoriali finestra di attenzione immagine acquisita

18. Datteri, E., Teti, G., Laschi, C., Tamburrini, G., Dario, P., Guglielmelli, E. (2003), "Expected perception in robots: a biologically driven perception-action scheme", in Proceedings of ICAR 2003, 11th International Conference on Advanced Robotics, Vol. 3, pp. 1405-1410. Multi-level interaction between Neuroscience and Robotics real-time behavioral capabilitiesreactive, real-time purposeful behavior in the real world Feedback-based models of sensory-motor coordination in humans reactive architectures schemes the problem of delays computational costs of sensory-motor trasformation problems Neuroscience Robotics perception as comparison between predicted and actual sensory feedback perception as comparison between actual perception and EXPECTED PERCEPTION (EP) solutions

19. The Expected Perception scheme • Datteri, E., Asuni, G., Teti, G., Laschi, Dario, P., Guglielmelli, E. (2004), "Experimental Analysis of the Conditions of Applicability of a Robot Sensorimotor Coordination Scheme based on Expected Perception", in Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2004), IEEE, Sendai, Japan, pp. 1311-1316. • Datteri, E., Teti, G., Laschi, C., Tamburrini, G., Dario, P., Guglielmelli, E. (2003), "Expected perception: an anticipation-based perception-action scheme in robots", in IROS 2003, 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, pp. 934- 939. • Datteri, E., Teti, G., Laschi, C., Tamburrini, G., Dario, P., Guglielmelli, E. (2003), "Expected perception in robots: a biologically driven perception-action scheme", in Proceedings of ICAR 2003, 11th International Conference on Advanced Robotics, Vol. 3, pp. 1405-1410.

20. The Expected Perception scheme ACTION SENSOR PROCESSING MOTOR COMMAND GENERATOR SENSING WORLD

21. The Expected Perception scheme EXPECTED PERCEPTION INTERNAL MODEL <  + - ACTUATORS SENSOR PROCESSING MOTOR COMMAND GENERATOR SENSORS >  WORLD

22. EP systemflowchart

23. Robotic system and task • 8 d.o.f. robotic arm • Color camera • Task: pushing an object towards a target position pushing path object target

24. Prospect for a comparative analysis of the three visuomotor coordination schemes EP-based feedback-based no-feedback performance of the visuo-motor control scheme environmental variability

25. Quantitative analysis of the conditions of applicability of the EP scheme • For which level of environmental predictability the EP scheme can be fruitfully adopted? • Strategy: • Implement two visuo-motor coordination systems that execute the same task (pushing) • Feedback-based system • EP system • Measure the performances of the two systems in environments characterized by different levels of predictability • Identify the levels of environmental predictability in which the performance of the EP scheme is higher than that of the feedback-based scheme

26. Implementation of the (visual) feedback-based scheme • Thresholding • Segmentation via region growing • Detection of centroid position and object orientation

27. Position of the end-effector at time t (in the robot reference system) Position of the object centroid at time t (in the image reference system) Desired position of the end-effector (in the robot reference system) Transformation matrix from the robot to the image reference systems M Velocity of the end-effector at time t (in the robot reference system) EP generation Forward dynamic and output model

28. Implementation of the EP system: overview Real images (direct flow from the camera) Acquired images Expected Perception Difference

29. pixel-pixel difference over a rectangular attention window centered at the EP coordinates EP image acquired image Comparison error = number of colored pixels/number of pixels

30. The system is not affected by irrelevant movements irrelevant elements of the environment

31. The system is not affected by lighting conditions object shape

32. Environmental predictability high predictability low predictability 50g

33. Metrics for environmental predictability How much does the object deviate from a straight pushing trajectory? Deviation (error) with respect to the pushing trajectory: the higher the error, the lower the predictability of object motion EPT (cm) 50g 20g different weights inside the object modify the predictability of its motion during robot pushing

34. Metrics for system performance • Error on target (ET) • Error of position on path (EPP) • Distance of the object from the trajectory • Computational Reduction (CR) • How many processing steps are skipped? EPP Performance = (1 - EPP ) * KEPP + (1 - ET ) * KET +CR * KCR

35. Comparative results: EP based and feedback-based schemeComputational reduction 89,46% 86,62% 91,01% environment predictability

36. BRAINSTORMING • Coordinazione visuo-motoria • Interazione uomo-robot • Selezione di parametri di navigazione • Discriminazione di movimenti auto-indotti • Percezione tattile • Auto-localizzazione

37. Catching non-controlled objects: online trajectory prediction • Every 1ms the visual input is processed in order to find the position ro of the target • segmentation • extraction of the area of interest • computation of the image moments • The target trajectory is predicted online • The predicted trajectory is used to correct online the end-effector trajectory

38. Model-based visuomotor coordination based on neural networks • Wunsch, P., Winkler, S., Hirzinger, G. (1997), “Real-Time Pose Estimation of 3-D Objects from Camera Images Using Neural Networks”, in Proceedings of the 1997 IEEE International Conference on Robotics and Automation, pp. 3232-3237. • Kragic, D., Christensen H.I. (2002), "Model Based Techniques for Robotic Servoing and Grasping", in Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland, pp. 299-304.

39. Anticipazione e coordinazione visuomotoria • Sfruttare la conoscenza dei movimenti eseguiti dal robot per prevederne le conseguenze sensoriali

40. Percezione tattile • Alto numero di sensori tattili • Anticipazione motoria (“pre-shaping”) • Anticipazione sensoriale (presa e manipolazione di oggetti)

41. Interazione uomo-robot • Previsioni sulle conseguenze delle azioni motorie del robot possono essere usate per regolare l’interazione uomo-robot (es. parametri visco-elastici del robot) • Bracci robotici • Esoscheletri

42. Navigazione e agenti ‘a comportamenti’ • Rilevamento delle caratteristiche dell’ambiente per la • selezione di pesi comportamentali • selezione di strategie di apprendimento HI(follow-wall)[t0; t1] ^ INCREASES(turn-to-door); INCREASE(close-to-door); DECREASES(follow-wall)[t1; t2] ^ HI(turn-to-door); LO(follow-wall)[t2; t3] ^ INCREASES(go-through-door); DECREASES(turn-to-door); HI(close-to-door)[t3; t4] ^ DECREASES(go-through-door)[t4; t5] “The fact extraction technique does not presume or guarantee anything about the consistency of the facts that get derived over time. Achieving and maintaining consistency, and determining the ramifications of newly emerged facts remain issues that go beyond fact extraction. Pragmatically, we would not recommend to blindly add a fact as true to the fact base as soon as its defining chronicle has been observed. A consequent of a recognized defining chronicle should be interpreted as evidence for the fact or as a fact hypothesis, which should be added to the robot’s knowledge base only by a more comprehensive knowledge base update process, which may even reject the hypothesis in case of conflicting information.”

43. Auto-localizzazione • Percezione come ipotesi-test • Controlli di consistenza nei dati sensoriali • Controllo di ipotesi sulla posizione dell’agente in una mappa • Esplorazione collettiva di territori (un agente genera previsioni sensoriali che vengono poi testate da altri agenti)

44. Discriminazione sensoriale di movimenti auto-indotti • Lo spostamento dei sensori causato dalle azioni motorie del robot genera modifiche nelle letture sensoriali • Mediante l’uso di modelli forward queste modifiche possono essere “cancellate” dalle afferenze sensoriali • Esperimenti sulla navigazione guidata da visione (Stephan, Gross, 2001) • Altri tipi di sensori? Altri dominii (teste, braccia…)