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Human-Centered Robotics

Oussama KhatibArtificial Intelligence Laboratory

Department of Computer ScienceStanford University

.. in the human environment

Exploring the Human Environments

Safety & Performance

Sensing and Perception

Planning and Controlreal-time, unstructured world

real-time, many degrees of freedom

Compatibility with Human

Mechanisms and ActuationHuman-like skills

Interactivity & Human-Friendly

Safety

2

Safety

Performance

Competing?

Requirements

Human-Friendly Robots Human-Friendly Robots

• Dependable & Safe• Soft Actuators• Light Structures• Impact-reduction Skin• Low Reflected Inertia• Distributed Sensing• Good Performance

PumaConventional Geared Drive:

• Lighter structure• Large reflected

actuator inertia

JmotorNgear

Jlink

JmotorNgear

Jlink

(Jlink + N2Jmotor)Effective Inertia

Heavy structure

Technology Why Are Robotic Arms Unsafe?

Robot Collision

Head Injury Criteria (HIC)

Head Injury Criteria (HIC)

Inte

rface

Stif

fnes

s (k

N/m

)

Head Injury C

riteria Index

Effective Inertia (kg)

Puma

>20 cm compliant covering

Torq

ueM

agni

tude

Frequency

Actual Torque Requirements

Actuation Requirements

Torque Vs Frequency: Square Wave

+

Assumed Torque Requirements

Torq

ueM

agni

tud e

Frequency

3

ElasticCouplingElastic

Coupling Large BaseActuator

Large BaseActuator

Parallel Actuation

Small JointActuator

Small JointActuator

Distributed Macro Mini (DM2) Approach Robot Characteristics

Equivalent Mass-Spring Model

Robot Collision

• Effective Inertia• Effective Stiffness• Impact Velocity

Manipulator Safety Index (MSI) DM2 New Testbed

“the high capacity of a large robot with the fast dynamics and safety of a small one”

DM2 vs. PUMA 560: Effective Mass Comparison

PUMA 560 (link 2 and link 3)

HFR (conventional actuation)

HFR (DM2 actuation)

Maximum effective mass:

PUMA 560: 24.37 kg

HFR (Macro actuation): 12.71 kg

HFR (DM2 actuation): 2.81 kg

10

20

30

30

210

60

240

90

270

120

300

150

330

180 0

Manipulator Safety Index (MSI)

DMM: 10x reduction in effective inertia

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10x reduction in effective inertia3x increase in position control bandwidth10x decrease in trajectory tracking error

SafetyAND

Performance

DistributedMacro-MiniActuation

DM2

DM2 Performance

• Whole-body control strategies

• Multi-contact and constraints

• Walking and Manipulation

Human-Like Structuresbranching and under-actuated

Task & Posture Decomposition

Whole-body Control

Task Dynamics and Control

Task Control

T FJΓ =

Task Dynamics

)ˆ ˆ ˆ( TaskV pF μ= Λ + +−∇ TaskVF −∇=F

x

xΛ F=pμ+ +

Task Dynamics – Branching Structures

x J⇒

1

2

.

m

xx

x

x

⎛ ⎞⎜ ⎟⎜ ⎟=⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠ ⇒ Λ

5x

5f

2x2f

3x

3f1x 1f

6x6fxΛ F=pμ+ +

12 1

21 2

11

22

1 2

...

......................

...

L

L

L L LL

Λ Λ⎛ ⎞⎜ ⎟Λ Λ⎜ ⎟Λ =⎜ ⎟⎜ ⎟Λ Λ⎝ ⎠

ΛΛ

Λ

5

Control Structure

task postuT Ttask task reJ NF= +Γ Γ

0taskx =⇒

Decomposition of torque vector

Dynamic Consistency

Task and Posture Control

Whole-body Control

Task Energy

Posture Energy

no joint trajectories⇒

DynamicallyDecoupled

Learning from the human

Posture Energy?

Human Natural Motion

Motion Capture Specific MotionMotion Characteristics

HumanMotionCapture

HumanDynamicModel

Musclephysiology

Skeletalphysiology

Marker dataHuman motion

Human Motion Characterization

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Motion capture

Simulation 79 DOF and 136 MusclesBiometric Data & Bone Geometry

Dynamic simulation

In motion, humansminimize fatigue, under physical and “social” constraints

Learning from the Human

Physiology-based Posture Field

Physiology-based Posture Field

T FJΓ =TL mΓ =

A Task, F:

Muscle actuation:

cNMuscle capacities: Configuration-dependenttorque bounds

Physiology-based Posture Field

Human posture is adjusted to reduce muscular effort

2E cm=Human-muscular Energy minimized:

Function of physiology, mechanical advantage, and task

2 1)( ) ]([Tc

TTL NE q F J L FJ− −=

Data from Subjects

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Data from Subjects Validation - Arm Effort

Validation - Arm Effort Validation – whole-body effort

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SAI EnvironmentDynamic simulation, control, & haptics

SAI Neuromuscular Library

Constraints and Priorities

Self Collision

Collision Avoidance

Collision Avoidance

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Elastic Planning Elastic Planning

Interactive Haptic Simulation Contact/Collision Resolution

Multi-point contactmulti-articulated body

Single point contactwith a single body

Interactive

Efficient Dynamic AlgorithmsSimulation, Contact Resolution, and Control

m1 m2

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Crash TestsIntegrationof Walking

Multi-Contact Whole-body ControlIntegration of Whole-Body Control & Walking

Under-actuated Balance Reaction forces

Multi-Contact Whole-body ControlIntegration of Whole-Body Control & Walking

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.. going up! .. walking, motion, contacts!

Walking jumping!

Thank You!