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

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Presentation on theme: "Learning by imitation: Computational Modeling And Robotics Aude Billard Computer Science Department Program of Neuroscience University of Southern California."— Presentation transcript:

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 To what extend is biological inspiration useful? Is a model of human imitation useful for robotics? The imitator robot Should the robot have the same body configuration? EPFL, ASL Pygmalion Robot Lausanne, Switzerland 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 Robotics Motion Studies Computational Modeling Imitation Learning Modeling Implementing 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 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 Motion studies on human imitation In collaboration with James Gordon, Department of Biokinesiology, USC Steve Boker, Univ. of Notre Dame, Indiana

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

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

12 Hypothesis: We have a model of the human body kinematics and dynamics Question III: How do we recognize biological motions from non biological ones? 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: 1. Goal-directed: grasping, kicking an object 2. Functional: Tying shoes, stacking boxes 3. Abstract: dance, highly skilled motion Imitation Learning Learning new motions requires both eccentric and intrinsic information, as well as information on amplitude, speed, acceleration. Task-dependent method of analysis 1.Eccentric: End-point trajectories Principal Component Analysis 2. Egocentric: joint trajectories Cross-correlation, phase shift

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

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

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

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

19 Schematic of the model

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

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

23 Example: Imitating Human Arm Motions

24 Imitation of Gestures Gesture 1Gesture 2Gesture 3

25 Recurrent NN Sequence learning Generalization across movements

26 DRAMA: Dynamical Recurrent Associative Memory Architecture - 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

27 DRAMA: Unit Activation function Decay of activity Input Thresholds

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

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

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

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

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

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

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

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, SwitzerlandJean-Daniel Nicoud, director of DIDEL SA

41 ROBOTA: TECHNICAL SPECIFICATIONS

42

43 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 lIndustrie, French National Science Museum November 2001 – July 2003

45 Robota: Applications I French National Science Museum - La Cite des Sciences et de Lindustrie LANGUAGE GAME

46 Robota: Applications III Aurora Project Dr. Kerstin Dautenhahn, Univ. of Hertfordshire 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 Robots USC – CS499 Undergraduate Computer Science Degree (4th Year) 30 Students. Equipment (Robots + PCs) supported by a «Innovative Teaching » grant from Intel Corp.

48 ROBOTA Class Syllabus Assignment 2Conversational Robot"

49 ROBOTA Class Syllabus Assignment 4Imitator Robot"

50 Robota drives a car

51 Carbota Robota leg motors are wired to the remote control car motors: Kameleon 376 Processor Board (K-Team) is attached to the front of Robota

52 Carbota So pretty!Robota sitting in the remote controlled car.

53 Final projects Robota Counting Game Robota Learns to Dress up Robota Does Cross-Country skiing

54 ROBOTA UserGuide

55 Webots-Robota : The Simulator Webots is a product of Cyberbotics (www.cyberbotics.com)

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

57 Summary Biology Robotics Take inspiration from Biology and Psychology to design robots which can learn from interacting with humans and with other robots Keywords: Learning, human-robot and multi-robot interactions To use robotic tools (robots and realistic simulations) to build biologically plausible, computational models of animal ability. Keywords: Computational neurobiology, biologically inspired robotics Biology Robotics


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