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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 robot interaction
Human-Robot Interaction

robot

planning

behaviors

tasks

human

actuators

sensors

human models for hri1
Human Models for HRI

robot

choose cost function

estimate human state

predict human response

planning

behaviors

tasks

human

learning by demonstration

actuators

sensors

what we want to model
What We Want to Model
  • mechanical properties
    • compliance
  • physiology
    • actuators/sensors
    • motor control
  • behavior
  • perception
  • emotion
what we want to model1
What We Want to Model
  • mechanical properties
    • compliance
  • physiology
    • actuators/sensors
    • motor control
  • behavior
  • perception
  • emotion
musculoskeletal model
Musculoskeletal Model

[Nakamura et al. 2005]

155 DOF skeleton

989 muscles

algorithms

inverse kinematics

inverse dynamics

muscles tendons ligaments
Muscles/Tendons/Ligaments

989 muscles

50 tendons

117 ligaments

[Nakamura et al. 2005]

[Murai et al. 2007]

see through mirror
“See-Through Mirror”

[Murai et al. 2010]

motor control system
Motor Control System

planning

reflex

100ms+

~30ms

muscle

tensions

musculoskeletal system

somatosensory

information

motion

motor control system1
Motor Control System

planning

???

reflex

100ms+

~30ms

inverse dynamics

muscle

tensions

musculoskeletal system

motion capture

somatosensory

information

motion

motor control system2
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

motor control system3
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
somatosensory reflex model
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]

identification
Identification

unknown weights

inputs

delay

+

+

delay

+

+

motion capture data

outputs

muscle tensions

delay

+

+

[Murai et al. 2008]

patellar tendon reflex
Patellar Tendon Reflex

hit!

[Murai et al. 2008]

simplified model
Simplified Model

[Murai et al. 2010 (submitted)]

(x0.03)

saggital plane

7 muscles in each leg (14 muscles)

learned from normal walking motion

reproducing walk
Reproducing Walk

simulated using the output of the reflex model

trip simulation
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
trip simulation1
Trip Simulation

elevating strategy

lowering strategy

what we learned
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

human models for hri2
Human Models for HRI

robot

choose cost function

estimate human state

predict human response

planning

behaviors

tasks

human

learning by demonstration

actuators

sensors

balancing while tracking
Balancing while Tracking

[Yamane, Hodgins 2009] [Yamane, Anderson, Hodgins 2010]

balancing while tracking1
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
what we learned1
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

mapping to non humanoids
Mapping to Non-Humanoids

[Yamane, Ariki, Hodgins 2010]

mapping to non humanoids1
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

mapping to non humanoids2
Mapping to Non-Humanoids

dynamics

optimization

static mapping

mocap

select key poses

create character’s

key poses

learn mapping function

mapping to non humanoids3
Mapping to Non-Humanoids

anger / disgust / fear / happiness / sadness / surprise

anger

disgust

fear

sad

what we learned2
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?
other random thoughts
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?