1. SA-1 Robotic Self-Perception and Body Scheme Learning Jürgen Sturm Christian Plagemann Wolfram Burgard University of Freiburg Germany With annotated questions

2. Outline Presentation of current research Planned journal articles Future research / experiments Clarification of Concepts, e.g., - Mirror Neurons - Body Schema Possible support of our theory from a psychological point of view? Psychological evidence Co-experiments human/robot General Brainstorming

3. Motivation Existing robot models are typically specified (geometrically) in advance calibrated manually

4. Motivation Problems with fixed robot models: Wear-and-tear wheel diameter, air pressure Recovery from failure malfunctioning actuators Tool use extending the model Unknown model re-configurable robots

5. Problems with fixed robot models: Wear-and-tear wheel diameter, air pressure Recovery from failure malfunctioning actuators Tool use extending the model Unknown model re-configurable robots Similar problems in humans/animals? Motivation

6. Problems with fixed robot models: Wear-and-tear wheel diameter, air pressure Recovery from failure malfunctioning actuators Tool use extending the model Unknown model re-configurable robots Similar problems in humans/animals? Motivation growth, aging injured body parts writing riding a bike

7. Related Work Neuro-physiology Mirror neurons [Rizzolatti et al., 1996] Body Schemes [Maravita and Iriki, 2004] Robotics Self-calibration [Roy and Thrun, 1999] Cross-modal maps [Yoshikawa et al., 2004] Structure learning [Dearden and Demiris, 2005] Clarification of concepts Better references? Good primer? Where else? E.g., - Self-configuring software - Language acquisition

8. Problem motivation Fixed-model approaches fail when parameters change over time geometric model is not available Bootstrapping of the body scheme and Life-long adaptation using visual self-observation Our Contribution

9. Sense 6D Poses Act Joint angles Think Bootstrap, monitor, and maintain internal representation of body Problem Description Motor babblingSelf-observation

10. Problem Formulation Visual self-perception of n body parts: Actuators (m action signals): Learn the mapping p ( X 1 ;:::; X n j a 1 ;:::; a m ) X 1 ;:::; X n 2 R 4 £ 4 Body pose Configuration a 1 ;:::; a m 2 R Which brain area does this mapping?

11. Existing Methods Analytic model + parameter estimation Function approximation Nearest neighbor Neural networks Requires prior knowledge High-dimensional learning problem Requires large training sets

12. Body Scheme Factorization Idea: Factorize the model We represent the kinematic chain as a Bayesian network Local models similar to mirror neurons?

13. Bootstrapping Learning the model from scratch consists of two steps: 1.Learning the local models (conditional density functions) 2.Finding the network/body structure Mirror neurons? Synaptic pathways?

14. Learning the Local Models Using Gaussian process regression Learn 1D  6D transformation function for each (action, marker, marker) triple p ( ¢ 12 j a 1 ) = p ( X ¡ 1 1 X 2 j a 1 )

15. Finding the Network Structure Select the most likely network topology Corresponding to the minimum spanning tree Maximizing the data likelihood p ( M j D )

16. Model Selection

17. 7-DOF example Fully connected BN

18. Model Selection 7-DOF example Fully connected BN Selected minimal spanning tree More natural, incremental algorithm? E.g., simulated synaptic growth..

19. Forward Kinematics Purpose: prediction of end-effector pose in a given configuration Approach: integrate over the kinematic chain in the Bayesian network by concatenating Gaussians approximate the result efficiently by one Gaussian p ( X n j X 1 ; a 1 ;:::; a m ) = Z ::: Z p M 1 p M 2 ::: d X 2 ;:::; d X n ¡ 1

20. Inverse Kinematics Purpose: Generate motor commands for reaching a given target pose Approach: Estimate Jacobian of end- effector using forward kinematics prediction Use standard IK techniques Jacobian pseudo-inverse rX n ( a ) = · @ X n ( a ) @ a 1 ;:::; @ X n ( a ) @ a m ¸

21. Experiments

22. Evaluation: Forward Kinematics Fast convergence (approx. 10-20 iterations) High accuracy (higher than direct perception)

23. Evaluation: Inverse Kinematics Accurate control using bootstrapped body scheme

24. Life-long Adaptation Robot’s physical properties will change over time Predictive accuracy of body scheme needs to be monitored continuously Localize mismatches in the Bayesian network Re-learn parts of the network Body Schema Plasticity in humans/animals Physiology? Anatomy?

25. Life-long Adaptation Initial Error is detected and is localized Robot re-learns some local models Similar problem? Recovery after lesions to the brain?

26. Life-long Adaptation

27. Evaluation Quick localization of error Robust recovery Recovery time plot for a human after body “deformation”?

28. Summary Novel approach learning body schemes from scratch using visual self-perception Model learning using Gaussian process regression Model selection using data likelihood as criterion Efficient adaptation to changes in robot geometry Accurate prediction and control

29. Future Work Active self-exploration, optimal control, POMDPs Marker-less self-perception Moving robot Tool use

30. Future work: Tool Use Using tools requires dynamic extensions of the body scheme

31. Future research / experiments Tool use Writing with a pen Approach: Find Silhouette of Pen Detect tool-tip Assume rigid tool Learn geometric transformation Demonstration: Write/paint on whiteboard with pens of different size and shape

32. Student projects (1) Tutoring Evasion Maneuvers using Tactile Sensors (Frederic Dijoux)

33. Student projects (2) Model-Free Control for Robotic Manipulators using Nearest-Neighbor methods (Hannes Schulz and Lionel Ott)

34. Student projects (3a) Dynamically adding repellant end-effectors (Clemens Eppner)

35. Student projects (3b) Programming by Demonstration (Clemens Eppner)

36. Student projects (3c) Programming by Demonstration (Clemens Eppner)

37. Student projects (3d) Programming by Demonstration (Clemens Eppner)

38. Student projects (4a) Object Recognition using Tactile Sensors (Alexander Schneider)

39. Student projects (4b) Object Recognition using Tactile Sensors (Alexander Schneider)

40. Student projects (5a) Grasping objects using Visual Servoing (Nikolas Engelhard) (Video courtesy of TU Dortmund)

41. Student projects (5b) Grasping objects using Visual Servoing (Nikolas Engelhard) (Video courtesy of TU Dortmund)

42. Planned journal article Special Issue “Journal of Physiology” Neuro-Robotics Symposium – Sensorimotor Control, July 2008, Freiburg Two reviewers, one from neuro-biology, one from engineering, Deadline: 22.10.2008 Article Content: Similar to this presentation Stronger focus on mirror neurons and body schemas in humans/animals Support from psychological point of view?

43. Possible journal article Special Issue “Autonomous Mobile Manipulation” Journal “Autonomous Robots” Deadline: 15.12.2008 Article Content (if at all): Focus on Model selection?..

44. Brainstorming Psychological input Co-experiments human/robot Joint (student) project(s)?..