talk by giorgio metta
TRANSCRIPT
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iCuba shared platform for research in robotics & AI
GenoaJune 25, 2015
Giorgio Metta & the iCub teamIstituto Italiano di TecnologiaVia Morego, 30 - Genoa, Italy
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we have a dream
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the iCub
6/25/2015 4
price: 250K€30 iCubdistributed since 2008about 3-4 iCub’s/year
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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)
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why humanoids?
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• scientific reasons – e.g. elephants don’t play chess
• natural human-robot interaction
• challenging mechatronics
• fun!
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why open source?
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• repeatable experiments
• benchmarking
• quality
this resonates with industry-grade R&D in robotics
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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
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Yet Another Robot Platform
6/25/2015 11
• 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
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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
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iCub sensors
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torque control
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0 ∙
∙
∙ ∙ ∙
∙ ∙
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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
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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
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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
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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)
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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.
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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
∈
. .
• 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
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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
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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
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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
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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
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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
)
OF presentOF causalOF futureOF Independent
?
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… 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
. ove
r cla
sses
)
OF presentOF causalOF futureOF Independent
??
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Experiments on affordances
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Experiments on affordances
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Experiments on affordances
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Experiments on affordances
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3D vision for grasping
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Input Segmentation Disparity
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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
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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.
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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?”
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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
plet
edpr
ojec
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
newprojects