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iCuba shared platform for research in robotics & AI
Woods Hole, MA
Giorgio Metta & the iCub teamIstituto Italiano di TecnologiaVia Morego, 30 - Genoa, Italy
1
we have a dream
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2
the iCub
price: 250K€30 iCubdistributed since 2008about 3-4 iCub’s/year
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3
why is the iCub special?• 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)
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4
why humanoids?• scientific reasons
– e.g. elephants don’t play chess
• natural human-robot interaction
• challenging mechatronics
• fun!
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5
why open source?• repeatable experiments
• benchmarking
• quality
this resonates with industry-grade R&D in robotics
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Yet Another Robot Platform• 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
© Bryan Adams. All rights reserved. Thiscontent is excluded from our CreativeCommons license. For more information,see https://ocw.mit.edu/help/faq-fair-use/.
Courtesy of Laboratory forIntegrated Advanced Robotics.Used with permission.
© RobotCub Consortium. License GPL v2.0.This content is excluded from our CreativeCommons license. For more information,see https://ocw.mit.edu/help/faq-fair-use/.
Image of Tibi Robot removeddue to copyright restrictions.
Image is in the public domain.
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exploit diversity: portability• 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
Projectdescription
(.txt)
LINUX:Makefiles,Kdevelopfiles, ...
WINDOWS:MSVC files,Borland files,
...
OSX:Makefiles,
Xcode files, ...
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wiki & manual
SVN & GIT
part lists
drawings
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iCub sensors
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Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/.
10
the robot skin
crot
apac
i
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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11
© Source Unknown. All rights reserved.
This content is excluded from our Creative
Commons license. For more information,see https://ocw.mit.edu/help/faq-fair-use/.
© Source Unknown. All rights reserved.
This content is excluded from our Creative
Commons license. For more information,see https://ocw.mit.edu/help/faq-fair-use/.
© Source Unknown. All rights reserved.
This content is excluded from our Creative
Commons license. For more information,see https://ocw.mit.edu/help/faq-fair-use/.
© RobotCub Consortium. License GPL v2.0. This content is excluded from our Creative
Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/.
12
Fabric, conductive+protective
Inner support for PCB
PCB
fingernailCDC chip
Plastic interface transforming to curved shape
12 sensors14.5 x 13 mm
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© RobotCub Consortium. License GPL v2.0. This content is excluded from our Creative
Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/.
learning dynamics
Six axis F/T sensor
Inertial sensor
• 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
Courtesy of World Scientific Publishing Company. Used with permission.
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incremental experiments
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temperature compensation
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dataset
Human“Teacher”
MotionDetection
Verbal (weak) Supervision
sprinkler
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methodsLocal Coding-Pooling Coding Pooling
e.g. SIFT, HOG,SURF
e.g. VQ, SC,LLC
e.g. SpatialPyramid
ImageConvolution, NL, Pooling
Mainstream Object Recognition Pipelines:
e.g. SVM, RLS
ClassifierImage
Convolution, NL, Pooling
Convolutional Neural Networks:
Representation
e.g. SVM, RLS
ClassifierRepresentation
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exploiting continuity in time
sprinkler sprinkler sponge sprinkler sprinkler sprinkler
0.5 sec
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incremental learning
• 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:
daya
ccu
racy (
avg
. o
ve
r cla
sse
s)
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
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3D vision for grasping
Input Segmentation Disparity
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force reconstruction
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Photo of researchers removed due to copyright restrictions. Please see the video.
23
MIT OpenCourseWarehttps://ocw.mit.edu
Resource: Brains, Minds and Machines Summer CourseTomaso Poggio and Gabriel Kreiman
The following may not correspond to a p articular course on MIT Openprovided by the author as an individual learning resource.
CourseWare, but has been
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