Talk by Giorgio Metta

iCuba shared platform for research in robotics & AI

GenoaJune 25, 2015

Giorgio Metta & the iCub teamIstituto Italiano di TecnologiaVia Morego, 30 - Genoa, Italy

we have a dream

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the iCub

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price: 250K€30 iCubdistributed since 2008about 3-4 iCub’s/year

why is the iCub special?

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• hands: we started the design from the hands– 5 fingers, 9 degrees of freedom, 19 joints

• sensors: human-like, e.g. no lasers– cameras, microphones, gyros, encoders, force, tactile…

• electronics: flexibility for research– custom electronics, small, programmable (DSPs)

• reproducible platform: community designed− reproducible & maintainable yet evolvable platform − large software repository (~2M lines of code)

why humanoids?

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• scientific reasons – e.g. elephants don’t play chess

• natural human-robot interaction

• challenging mechatronics

• fun!

why open source?

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• repeatable experiments

• benchmarking

• quality

this resonates with industry-grade R&D in robotics

open source

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series-elastic actuators

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• C spring design• 320Nm/rad stiffness

• features:– stiffness by design– no preloading necessary

(easy assembly)– requires only 4 custom

mechanical parts– high resolution encoder for

torque sensing

Yet Another Robot Platform

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• YARP is an open-source (LGPL) middleware for humanoid robotics

• history

– an MIT / Univ. of Genoa collaboration

– born on Kismet, grew on COG, under QNX

– with a major overhaul, now used by the iCub project

• C++ source code (some 400K lines)

• IPC & hardware interface

• portable across OSs and developmentplatforms

2000-2001

2001-2002

2003

2004-Today

exploit diversity: portability

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• operating system portability:– Adaptive Communication Environment,

C++ OS wrapper: e.g. threads, semaphores, sockets

• development environment portability:– CMake

• language portability:– via Swig: Java (Matlab), Perl, Python, C#

C/C++library

C/C++library

C/C++library

C/C++library

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wiki & manual

SVN & GIT

part lists

drawings

iCub sensors

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torque control

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learning dynamics

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• learning body dynamics– compute external forces– implement compliant control

• so far we did it starting from e.g. the cad models– but we’d like to avoid it

our method in 4 easy steps

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• regularized least square

• kernelized

• approximate kernel

• make it incremental

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properties• O(1) update complexity w.r.t. # training samples

• exact batch solution after each update

• dimensionality of feature mapping trades computation for approximation accuracy

• O(D²) time and space complexity per update w.r.t. dimensionality of feature mapping

• easy to understand/implement (few lines of code)

• not exclusively for dynamics/robotics learning!

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batch experiments• 3 inverse dynamics datasets: Sarcos, Simulated Sarcos,

Barrett [Nguyen-Tuong et al., 2009]

• approximately 15k training and 5k test samples

• comparison with LWPR, GPR, LGP, Kernel RR

• RFRR with 500, 1000, 2000 random features

• hyperparameter optimization by exploiting functional similarity with GPR (log marginal likelihood optimization)

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datasets

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incremental experiments

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incremental experiments

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temperature compensation

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summary• Fast prediction and model update of RFRR

– 200RF: 400μs– 500RF: 2ms– 1000RF: 7ms

• Non-stationary: thermal sensor drift in force components• Rapid convergence of RFRR• No further gain by using additional random features

(problem specific)

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experiments & model validation

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static configuration:

an additional six axis F/T sensor is placed at the end effector tomeasures the external wrenches we

Joint 0 Joint 1 Joint 2 Joint 3E (τj-τft) 0.127 Nm -0.049 Nm -0.002 Nm -0.032 Nm

σ (τj-τft) 0.186 Nm 0.131 Nm 0.013 Nm 0.042 Nm

E (τj-(τm+τe)) 0.075 Nm -0.098 Nm -0.006 Nm 0.006 Nm

σ (τj-(τm+τe)) 0.191 Nm 0.173 Nm 0.020 Nm 0.032 Nm

additional F/T sensor at the end-effector

in this experiment we consider the following quantities:

• joint torques measured by the joint torque sensors: tj• joint torques computed from the arm F/T sensor: tFT• joint torques estimated thought the additional F/T sensor located

at the end effector: te=JTwe• joint torques predicted by the arm model (no external forces): tm

the robot skin

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capa

cito

r

electrodes: etched on a flexible PCBparameters: shape, folding, etc.

soft material: e.g. siliconeparameters: dielectric constant, mechanical stiffness, etc.

ground plane: e.g. conductive fabricparameters: mechanical properties, impedance, etc.

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principle

lots of sensing points

structure of the skin

tests of various materials

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0 2 4 6 8 10 12 14 16 180

20

40

60

80

100

120

LC [kPa]

C

[bit]

Soma Foama HardPDMSSoma Foama SoftNeoprene2mmSpongeRubberLycraNeopreneGrid2mmNeoGridNeobulkLycraNeoGridNeobulkLycra2Neoprene2mmLycraNeopreneGrid3mm

Others (discarded)

latest implementation

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advantages:• good performance: gluing is made with industrial machines, no hysteresis due to glue• production: automatic and reliable• mounting and replacing is easy, easy ground connection • protective layer can be of different materials increase reliability• customizations: surface can be printed

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skin calibration

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performed manually by poking the robot with a tool

works iteratively with different datasets taken in different robot positions

A. Del Prete, S. Denei, L. Natale, F. Mastrogiovanni, F. Nori, G. Cannata, and G. Metta. “Skin spatial calibration using force/torque measurements”, in Intelligent Robots and Systems (IROS), 2011

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project CoDyCo, Nori et al.

floating base robots

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point to point movements

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ipopt

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• quick convergence (<20ms)• scalability• singularities and joints bound handling• tasks hierarchy• complex constraints

∈

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• merges joint and Cartesian space trajectories

min,

12

. . ∙

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Please put those into the dishwashing

machine

Could you please help me with the

TV set?

The iCub puts the plates into the dishwashing machine

Trueswell, J. C., & Gleitman, L. R. (2007) Learning to parse and its implications for language acquisition,in G. Gaskell (ed.) Oxford Handbook of Psycholing. Oxford: Oxford Univ. Press.

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The iCub puts the plates into the dishwashing machine

actions objects tools

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actions

learning actions

recognizing actions

objects

learning objects

recognizing objects

tools

learning tools

using tools

lear

nus

e

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Ranked 2nd at Microsoft Kinect Demonstration Competition, CVPR 2012 Providence, Rhode Island

object recognition

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self-supervised strategies

kinematics motion

Human Robot Interaction is a new and natural application for visual recognitionIn robotics settings strong cues are often available, therefore object detectors can be avoidedRecognition as tool for complex tasks: grasp, manipulation, affordances, pose

S.R. Fanello, C. Ciliberto, L. Natale, G. Metta – Weakly Supervised Strategies for Natural Object Recognition in Robotics, ICRA 2013C. Ciliberto, S.R. Fanello, M. Santoro, L. Natale, G. Metta, L. Rosasco - On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics, IROS 2013

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dataset

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iCubWorld dataset (2.0)

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• Growing dataset collecting images from a real robotic setting• Provide the community with a tool for benchmarking visual recognition systems in robotics• 28 Objects, 7 categories, 4 sessions of acquisition (four different days)• 11Hz acquisition frequency.• ~50K Images http://www.iit.it/en/projects/data-sets.html

methods

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methods

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Sparse Coding [yang et al. ’09]Learned on iCubWorldSparse Coding [yang et al. ’09]Learned on iCubWorld

Overfeat [sermanet et al. ’14]Learned on ImagenetOverfeat [sermanet et al. ’14]Learned on Imagenet

some questions• Scalability. How do iCub recognition capability

decrease as we add more objects to distinguish?• Can we use assumptions on physical continuity to

make recognition more stable?• Incremental Learning. How does learning during

multiple sessions affect the system recognition skills?

• Generalization. How well does the system recognize objects “seen” under different settings?

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first results

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We started by addressing instance recognition

performance wrt # of objects

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exploiting continuity in time

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incremental learning

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• Present: test on current day• Causal: test on current and

past days• Future: test on future days

(current not included)• Independent: train & test on

current day only

Cumulative learning on the 4 days of acquisition. Tested on:

1 2 3 4

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

day

accu

racy

(avg

. ove

r cla

sses

)

OF presentOF causalOF futureOF Independent

what about a week?

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1 2 3 4 5 6 7

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

day

accu

racy

(avg

. ove

r cla

sses

)


?

… or a month?

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1 6 11 16 21 26 30

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

day

accu

racy

(avg

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??

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Experiments on affordances

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3D vision for grasping

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Input Segmentation Disparity

grasping

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Gori,I; Pattacini, U; Tikhanoff, V; Metta G; (submitted 2013) Ranking the Good Points: A Comprehensive Method for Humanoid Robots to Grasp Unknown Objects 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)

point cloud from stereominimum bounding box

segmentationsurface extraction

grasp points from curvature+ bias (task dependent)

grasp selection from experience

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force reconstruction

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raw data

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GP approximation

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from: Fogassi L., Gallese V., Fadiga L., Luppino G., Matelli M., Rizzolatti G. Coding of peripersonal space in inferior premotor cortex (area F4). Journal of Neurophysiology 76 (1) 1996.

From: Graziano 1999

From: Graziano et al. 2006

spinal reflexes

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walking behavior: cat rehabilitated to walkafter complete spinal cord transection

wiping reflex: an irritating stimuluselicits a wiping movement preciselydirected at the stimulus location

from: Poppele, R., & Bosco, G. (2003). Sophisticated spinal contributions to motor control. Trends in Neurosciences, 26(5), 269-276.

visuo-tactile fusion

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double touch

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From two fixed-base chain to a single floating-base serial chain 12 dof

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receptive fields• Receptive field: a cone that extends up to 0.2m and angle of 40

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single taxel model

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no noise

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noisy data

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with double touch

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visual tracker

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external stimulation

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extending peripersonal space

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what future?

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iCub2.0

iCub3.0

now the future?

iCub new tech

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“How old are you?” she wanted to know.

“Thirty-two,” I said.

“Then you don’t remember a world without robots. There was a time when humanityfaced the universe alone and without a friend. Now he has creatures to help him;stronger creatures than himself, more faithful, more useful, and absolutely devoted tohim. Mankind is no longer alone. Have you ever thought of it that way?”

external funding

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– RobotCub, grant FP6-004370,http://www.robotcub.org

– CHRIS, grant FP7-215805, http://www.chrisfp7.eu

– ITALK, grant FP7-214668, http://italkproject.org

– Robotdoc, grant FP7-ITN-235065http://www.robotdoc.org

– Roboskin, grant FP7-231500http://www.roboskin.eu

– eMorph, grant FP7-231467http://www.emorph.eu

– Poeticon, grant FP7-215843http://www.poeticon.eu

• more information: http://www.iCub.org

com

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edpr

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ts

– Poeticon++, grant FP7-288382http://www.poeticon.eu

– Xperience, grant FP7-270273http://www.xperience.org

– EFAA, grant FP7-270490http://efaa.upf.edu/

– Codyco, grant FP7-600716http://www.codyco.eu

– Tacman, grant FP7-610967– Wysiwyd, grant FP7-612139– Walk-man, grant FP7-611832– Koroibot, grant FP7-611909

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