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Learning by imitation: Computational Modeling And Robotics

Learning by imitation: Computational Modeling And Robotics. Aude Billard Computer Science Depar t ment Program of Neuroscience University of Southern California Los Angeles. Robot Learning By Imitation. Teaching a robot complex motor skills by demonstration.

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Learning by imitation: Computational Modeling And Robotics

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  1. Learning by imitation:Computational Modeling And Robotics Aude Billard Computer Science Department Program of Neuroscience University of Southern California Los Angeles

  2. Robot Learning By Imitation Teaching a robot complex motor skills by demonstration

  3. What Does It Take To Imitate? • What should we imitate? • Which features of the action are relevant? • What should we pay attention to?

  4. Finding the goal of the action Grasping an object Relevant Features: Hand-Object relationship

  5. Should the robot have the same body configuration? EPFL, ASL Pygmalion Robot Lausanne, Switzerland The imitator robot To what extend is biological inspiration useful? Is a model of human imitation useful for robotics? Kawato Erato Project ATR, Kyoto, Japan Humans and robots have different body dynamics

  6. Learning by imitation: Motivations Robotics: A means of transmitting motor skills - Coordinated behavior, implicit attentional mechanism - Natural means of interaction - No need of explicit programming Biology: Computational Neuroscience - Abstract model of primate ability to imitate - Neural mechanisms behind learning by imitation - Cut down the debate concerning imitation

  7. Computational Modeling Implementing Modeling Robotics Imitation Learning Motion Studies From Human To Robot

  8. Motion studies on human imitation Recording and analysis of kinematics of full body motion Collaborations: James Gordon, Department of Biokinesiology & Stefan Schaal, CS dept, USC Steve Boker, Univ. of Notre Dame, Indiana

  9. Motion studies on human imitationIn collaboration with James Gordon, Department of Biokinesiology, USC Steve Boker, Univ. of Notre Dame, Indiana Visual and motor representation of movements: • Sensitivity to body cues • Orientation of body, direction of limb motion • Eccentric versus intrinsic space • Tracking hand path versus joint angles

  10. Question I : What are themetrics behind imitation? Bekkering, Wholschlager & Prinz, Psycholoquia 2000 • Hypotheses: • Imitation is based on a hierarchy of goals. • It can be goal-directed, exact, partial. • The metric is task-dependent.

  11. Hypothesis Biological clues: body symmetries, limb orientation Question II: What mechanisms are behind the immediate body to body mapping? Results: Bias for mirror imitation. Two transformations of frames of reference only.

  12. Question III: How do we recognize biological motions from non biological ones? Hypothesis: We have a model of the human body kinematics and dynamics Results: Hand path only is too ambiguous an information to reconstruct completely the motion.

  13. Question IV: Which representation of movement? Hypotheses Reconstruction is based on a model of natural motion Basic, primitive patterns of motions: Coupled oscillation of limbs Results: No significant effect of display type on performance. Poor performance on in-phase/anti-phase patterns: bimanual coordination Non leading arm tends to produce the closest preferred pattern

  14. Motion studies on human imitation • Experiments: • Goal-directed: grasping, kicking an object • Functional: Tying shoes, stacking boxes • Abstract: dance, highly skilled motion • Task-dependent method of analysis • Eccentric: End-point trajectories • Principal Component Analysis • Egocentric: joint trajectories •  Cross-correlation, phase shift • Imitation Learning • Learning new motions requires both eccentric and intrinsic information, as well as information on amplitude, speed, acceleration.

  15. Computational Modeling Implementing Modeling Robotics Imitation Learning Motion Studies From Human to Robot

  16. Neural mechanisms behind learningbyimitation Hypotheses: 1. Common parametrisationto visual and motor systems - Body-centered reference frame - Coding of mvt in orientation, amplitude and speed - Mirror Neurons: visuo-motor mapping 2. Dynamic learningof motor commands - Coarse coding of information, movement sequence - Adaptation and combination of basic motor patterns

  17. Pre-Motor Cortex / Broca’s area: Visuo-motor transformation / Mirror Neurons Motor Cortex Somatotopic control SMA: Sequence learning Parietal Lobe: Eccentric visual coding Frontal Lobe: Decision Center Inhibition of motion Cerebellum: Timing, Sequencing Temporal Lobe (STS): Eccentric – Intrinsic visual Representation of movement Spinal Cord + Brain Stem: Basic motor patterns, CPG Locomotion, Reflexes High-Level representation of the brain mechanisms underlying imitation Functional and abstract model of the brain areas and their connection

  18. Motor Cortex Somatotopic control Parietal Lobe: Eccentric visual coding Frontal Lobe: Decision Center Inhibition of motion Cerebellum: Timing, Sequencing Temporal Lobe (STS): Eccentric – Intrinsic visual Spinal Cord Basic motor patterns, CPG Pre-Motor Cortex: Visuo-motor transform SMA: Sequence learning Brain Stem High-Level representation of the brain mechanisms underlying imitation Functional and abstract model of the brain areas and their connection

  19. Schematic of the model

  20. Neural Output Joint Angle Visual Processing Segmentation Filtering of Small Motions

  21. Motor Control Leaky-integrator neurons Spring and Damper Muscle Model (Lacquaniti & Soechting 1986) Flexor-extensor pair per degree of freedom (DOF) 41 DOFs simulator, 30 DOFs humanoid robot

  22. Visuo-Motor Transformation Learning Module Visuo-Motor Module Motor Module Visual Module Fixed transformation First order approximation of inverse dynamics

  23. Example: Imitating Human Arm Motions

  24. Imitation of Gestures Gesture 1 Gesture 2 Gesture 3

  25. Recurrent NN • Sequence learning • Generalization across movements

  26. Fully recurrent NN with self connections on each unit • Time-delay neural network: • Learning of complex time series and of spatio-temporal invariance • Hebbian Learning: on-line and on-board robot learning DRAMA: Dynamical Recurrent Associative Memory Architecture

  27. Input Decay of activity Thresholds Thresholds DRAMA: Unit Activation function

  28. Learning of a dance movement sequence Joint by joint segmentation 1st pattern 2nd pattern 3rd action pattern Learning actions: one by one CLMC-LAB Human Demonstration Replay of Recordings

  29. Learning of a dance movement sequence Learned Posture 1 1st pattern 2nd pattern Learned actions lead to the following postures Learned Posture 2 CLMC-LAB

  30. Learning sequences of actions Action A Action C Action A Action B Action B Action C Action D Action D Action E Action E Learned Motor Programs

  31. Learning sequences of actions Time-delay neural network Learn sequencing and timing of the action sequence w, t Action A Action A Action B Action B Action C Action C Action D Action D Action E Action E Fully Recurrent NN Connectivity is built on-line

  32. Imitate sequences of actions Action A Action C Action A Action B Action B Action C Action D Action D Action E Action E

  33. Improvise using the learned sequences of actions Action A Action C Randomly activate or shut down nodes to produce new action sequences Action A Action B Action B Action C Action D Action D Action E Action E

  34. Modeling: Summary • Data Segmentation: Finding the key features of motion • Change in speed and orientation, joint-based representation • Common parametrization of movements: visual and motor systems • Speed and direction of movement, joint-based representation • Reconstruction of movements:robustness against perturbation • Learning of actions:synchronous and sequential activations of limbs • Recombination of basic movements: improvisation

  35. Robota Computational Modeling Implementing Modeling Robotics Imitation Learning Motion Studies From Human to Robot

  36. Robota Clever Toy and Educational Toy

  37. Robota

  38. First Prototype Univ of Edinburgh, 1998

  39. Second Prototype LAMI - EPFL, 1999 In collaboration with Jean-Daniel Nicoud and Andre Guignard

  40. Robota – The Product DIDEL SA, Switzerland Jean-Daniel Nicoud, director of DIDEL SA

  41. ROBOTA: TECHNICAL SPECIFICATIONS

  42. ROBOTA: TECHNICAL SPECIFICATIONS

  43. ROBOTA: TECHNICAL SPECIFICATIONS Robota battery set attaches to the back of the Motor Board 7.2V,7x1.2NiCd

  44. Robota at the Museum La Cite des Sciences et de l’Industrie, French National Science Museum November 2001 – July 2003

  45. French National Science Museum - La Cite des Sciences et de L’industrie LANGUAGE GAME Robota: Applications I

  46. Aurora Project Dr. Kerstin Dautenhahn, Univ. of Hertfordshire www.aurora.com Robota: Applications III • Center for Fundamental Infant Development • Drs Demuth, Pena, Bradley, Turman • USC Dept of Biokinesiology and Physical Therapy • USC Premature Infant Follow-Up Pediatric Clinic

  47. Mechatronics: Programming Humanoids RobotsUSC – CS499Undergraduate Computer Science Degree (4th Year) 30 Students. Equipment (Robots + PCs) supported by a «Innovative Teaching » grant from Intel Corp.

  48. ROBOTA Class SyllabusAssignment 2“Conversational Robot"

  49. ROBOTA Class SyllabusAssignment 4“Imitator Robot"

  50. Robota drives a car

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