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Human Models for HRI

Human Models for HRI. Katsu Yamane. Human-Robot Interaction. robot. planning. behaviors tasks. human. actuators sensors. Human Models for HRI. robot. choose cost function estimate human state predict human response. planning. behaviors tasks. human. learning by demonstration.

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Human Models for HRI

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  1. Human Models for HRI Katsu Yamane

  2. Human-Robot Interaction robot planning behaviors tasks human actuators sensors

  3. Human Models for HRI robot choose cost function estimate human state predict human response planning behaviors tasks human learning by demonstration actuators sensors

  4. What We Want to Model • mechanical properties • compliance • physiology • actuators/sensors • motor control • behavior • perception • emotion

  5. What We Want to Model • mechanical properties • compliance • physiology • actuators/sensors • motor control • behavior • perception • emotion

  6. Musculoskeletal Model [Nakamura et al. 2005] 155 DOF skeleton 989 muscles algorithms inverse kinematics inverse dynamics

  7. Muscles/Tendons/Ligaments 989 muscles 50 tendons 117 ligaments [Nakamura et al. 2005] [Murai et al. 2007]

  8. Muscle Tension Estimation max tension 0

  9. “See-Through Mirror” [Murai et al. 2010]

  10. Motor Control System planning reflex 100ms+ ~30ms muscle tensions musculoskeletal system somatosensory information motion

  11. Motor Control System planning ??? reflex 100ms+ ~30ms inverse dynamics muscle tensions musculoskeletal system motion capture somatosensory information motion

  12. Motor Control System planning no high-level planning ??? model the reflex system reflex 100ms+ ~30ms inverse dynamics muscle tensions musculoskeletal system motion capture somatosensory information motion

  13. Motor Control System control by somatosensory reflex―why this may work? • (apparently) we don’t deliberately plan daily motions • planning is not required in normal situations • even if unusual things happen, planning does not take place until 100ms later

  14. Somatosensory Reflex Model anatomically correct connections spinal cord muscles delay + + delay + + motion capture data … … muscle tensions delay + + muscle spindle (muscle length) Golgi tendon organ (muscle tension) [Murai et al. 2008]

  15. Identification unknown weights inputs delay + + delay + + motion capture data outputs … … muscle tensions delay + + [Murai et al. 2008]

  16. Patellar Tendon Reflex hit! [Murai et al. 2008]

  17. Simplified Model [Murai et al. 2010 (submitted)] (x0.03) saggital plane 7 muscles in each leg (14 muscles) learned from normal walking motion

  18. Reproducing Walk simulated using the output of the reflex model

  19. Trip Simulation • human trip response [Eng et al. 1994] [Schilling et al. 2000] • early swing phase: elevating strategy to avoid collision with the obstacle • late swing phase: lowering strategy to land immediately and lift the other leg • initial response observed <100ms after trip (EMG) • likely to be generated by the normal walking controller

  20. Trip Simulation elevating strategy lowering strategy

  21. What We Learned somatosensory reflex model may be enough to control common behaviors somatosensory reflex model identified with walking motion generates reasonable responses to trips probably need contact information for more robust control

  22. Human Models for HRI robot choose cost function estimate human state predict human response planning behaviors tasks human learning by demonstration actuators sensors

  23. Balancing while Tracking [Yamane, Hodgins 2009] [Yamane, Anderson, Hodgins 2010]

  24. Balancing while Tracking [Yamane, Hodgins 2009] [Yamane, Anderson, Hodgins 2010] motion clip reference CoM reference joint trajectory joint torque command balance controller tracking controller robot current joint angles current CoM controller structure • balancing: based on inverted pendulum model • tracking: follow joint trajectory considering balancing

  25. What We Learned human motions are difficult to track having a good dynamics model is critical (especially for model-based force control) CoM + inverted pendulum might be a good abstraction for locomotion/balancing tasks

  26. Mapping to Non-Humanoids [Yamane, Ariki, Hodgins 2010]

  27. Mapping to Non-Humanoids [Lawrence 2003] GPLVM high-dimensional observation space low-dimensional latent space [Ek et al. 2007] GPLVM GPLVM human pose character pose latent space observation space 1 observation space 2 Gaussian process latent variable model (GPLVM) Shared GPLVM

  28. Mapping to Non-Humanoids dynamics optimization static mapping mocap select key poses create character’s key poses learn mapping function

  29. Mapping to Non-Humanoids anger / disgust / fear / happiness / sadness / surprise anger disgust fear sad

  30. What We Learned • range of character complexity • too complex: mapped motions don’t meet expectation • too simple: different strategies for expression • latent space for abstracting unstructured behaviors?

  31. Other Random Thoughts [Michalowski 2007] • managing expectations • story (task) design • appearance/functionality • appearance of robots • don’t care? • robotic? human-like? cartoonish? • functionality of robots • general-purpose? task-specific? • legged? wheeled?

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