iCub a shared platform for research in robotics & AI

Genoa June 25, 2015

Giorgio Metta & the iCub team Istituto Italiano di Tecnologia Via Morego, 30 - Genoa, Italy we have a dream

6/25/2015 3 the iCub

price: 250K€ 30 iCub distributed since 2008 about 3-4 iCub’s/year 6/25/2015 4 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)

6/25/2015 5 why humanoids? • scientific reasons – e.g. elephants don’t play chess

• natural human-robot interaction

• challenging mechatronics

• fun!

6/25/2015 6 why open source?

• repeatable experiments

• benchmarking

• quality

this resonates with industry-grade R&D in robotics 6/25/2015 7 open source

6/25/2015 8 6/25/2015 9 series-elastic actuators

• 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

6/25/2015 10 Yet Another Robot Platform

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

•history 2001-2002 – an MIT / Univ. of Genoa collaboration 2000-2001 – born on Kismet, grew on COG, under QNX – with a major overhaul, now used by the iCub project 2003 • C++ source code (some 400K lines) • IPC & hardware interface 2004-Today • portable across OSs and development platforms

6/25/2015 11 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++ C/C++ library library

C/C++ library 6/25/2015 12 wiki & manual

SVN & GIT

part lists

drawings

6/25/2015 13 iCub sensors

6/25/2015 14 torque control

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6/25/2015 15 learning dynamics

• 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

6/25/2015 16 our method in 4 easy steps

• regularized least square  T xwxf  2 1 2 min ywJ  Xw w 2 2  1   T T yXXXIw

• kernelized m   i , xxkcxf i  i 1   1 yIKc

 1 D  ,  Exxk T xzxz • approximate kernel ji   d iw d jw   D d 1  T T w xz     sin,cos  xwxw 

T 1 T • make it incremental  Iw  y + Cholesky rank-1 update

6/25/2015 17 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!

6/25/2015 18 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)

6/25/2015 19 datasets

6/25/2015 20 6/25/2015 21 6/25/2015 22 6/25/2015 23 incremental experiments

6/25/2015 24 incremental experiments

6/25/2015 25 temperature compensation

6/25/2015 26 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)

6/25/2015 27 experiments & model validation static configuration: an additional six axis F/T sensor is placed at the end effector to measures the external wrenches we 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 T at the end effector: te=J we • joint torques predicted by the arm model (no external forces): tm

additional F/T sensor at Joint 0 Joint 1 Joint 2 Joint 3 E(τ-τ ) 0.127 Nm -0.049 Nm -0.002 Nm -0.032 Nm the end-effector j ft 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

6/25/2015 28 the robot skin

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

soft material: e.g. silicone parameters: dielectric constant, mechanical stiffness, etc. capacitor

electrodes: etched on a flexible PCB parameters: shape, folding, etc.

6/25/2015 29 principle

lots of sensing points

structure of the skin

6/25/2015 30 tests of various materials

120 Soma Foama Hard PDMS Soma Foama Soft 100 Neoprene2mm SpongeRubberLycra NeopreneGrid2mm 80 NeoGridNeobulkLycra NeoGridNeobulkLycra2 Neoprene2mmLycra 60 NeopreneGrid3mm C [bit] C  40 Others (discarded)

20

0 0 2 4 6 8 10 12 14 16 18 LC [kPa]

6/25/2015 31 latest implementation

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

6/25/2015 32 6/25/2015 33 skin calibration

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 6/25/2015 34 6/25/2015 35 6/25/2015 36 6/25/2015 37 project CoDyCo, Nori et al.

6/25/2015 38 floating base robots

6/25/2015 39 6/25/2015 40 6/25/2015 41 6/25/2015 42 6/25/2015 43 point to point movements

6/25/2015 44 ipopt

∈ . .

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

1 min , 2 . . ∙

• merges joint and Cartesian space trajectories

6/25/2015 45 6/25/2015 46 6/25/2015 47 6/25/2015 48 Could you please help Please put me with the those into the TV set? dishwashing machine

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. 6/25/2015 49 The iCub puts the plates into the dishwashing machine

actions objects tools

6/25/2015 50 actions objects tools learn

learning actions learning objects learning tools use

recognizing actions recognizing objects using tools

6/25/2015 51 Ranked 2nd at Microsoft Kinect Demonstration Competition, CVPR 2012 Providence, Rhode Island

6/25/2015 52 object recognition

self-supervised strategies

kinematics motion

Human Robot Interaction is a new and natural application for visual recognition In robotics settings strong cues are often available, therefore object detectors can be avoided Recognition 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 2013 C. 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 6/25/2015 53 6/25/2015 54 dataset

6/25/2015 55 iCubWorld dataset (2.0)

• 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

6/25/2015 56 methods

6/25/2015 57 methods

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

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

6/25/2015 58 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?

6/25/2015 59 first results

We started by addressing instance recognition

6/25/2015 60 performance wrt # of objects

6/25/2015 61 exploiting continuity in time

6/25/2015 62 incremental learning

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

• Present: test on current day 1 • Causal: test on current and 0.95 past days 0.9 0.85 • Future: test on future days 0.8 (current not included) OF present 0.75 OF causal • Independent: train & test on 0.7 OF future OF Independent current day only 0.65

1 2 3 4 accuracy (avg. over classes) day

6/25/2015 63 what about a week?

1

0.95

0.9 0.85 ? 0.8 OF present 0.75 OF causal 0.7 OF future OF Independent 0.65

1 2 3 4 5 6 7 accuracy (avg. over classes) day

6/25/2015 64 … or a month?

1

0.95 0.9 0.85 ?? 0.8 OF present 0.75 OF causal 0.7 OF future OF Independent 0.65

1 6 11 16 21 26 30 accuracy (avg. over classes) day

6/25/2015 65 6/25/2015 66 6/25/2015 67 Experiments on affordances

6/25/2015 68 Experiments on affordances

6/25/2015 69 Experiments on affordances

6/25/2015 70 Experiments on affordances

6/25/2015 71 6/25/2015 72 3D vision for grasping

Input Segmentation Disparity

6/25/2015 73 grasping

point cloud from stereo segmentation minimum bounding box surface extraction grasp selection from experience

grasp points from curvature + bias (task dependent)

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) 6/25/2015 74 6/25/2015 75 force reconstruction

6/25/2015 76 raw data

6/25/2015 77 GP approximation

6/25/2015 78 6/25/2015 79 From: Graziano 1999

From: Graziano et al. 2006 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.

6/25/2015 81 spinal reflexes

walking behavior: cat rehabilitated to walk after complete spinal cord transection

wiping reflex: an irritating stimulus elicits a wiping movement precisely directed at the stimulus location

from: Poppele, R., & Bosco, G. (2003). Sophisticated spinal contributions to motor control. Trends in Neurosciences, 26(5), 269-276. 6/25/2015 82 visuo-tactile fusion

6/25/2015 83 double touch

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

6/25/2015 84 6/25/2015 85 6/25/2015 86 receptive fields

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

6/25/2015 87 single taxel model

6/25/2015 88 no noise

6/25/2015 89 noisy data

6/25/2015 90 with double touch

6/25/2015 91 visual tracker

6/25/2015 92 external stimulation

6/25/2015 93 6/25/2015 94 extending peripersonal space

6/25/2015 95 6/25/2015 96 6/25/2015 97 6/25/2015 98 what future?

iCub new tech

iCub3.0

iCub2.0

now the future?

6/25/2015 99 6/25/2015 100 6/25/2015 101 “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 humanity faced 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 to him. Mankind is no longer alone. Have you ever thought of it that way?”

6/25/2015 102 external funding – RobotCub, grant FP6-004370, – Poeticon++, grant FP7-288382 http://www.robotcub.org http://www.poeticon.eu – CHRIS, grant FP7-215805, http://www.chrisfp7.eu – Xperience, grant FP7-270273 – ITALK, grant FP7-214668, http://www.xperience.org http://italkproject.org – EFAA, grant FP7-270490 – Robotdoc, grant FP7-ITN-235065 http://efaa.upf.edu/ http://www.robotdoc.org – Codyco, grant FP7-600716 projects – Roboskin, grant FP7-231500 completed http://www.roboskin.eu http://www.codyco.eu – eMorph, grant FP7-231467 http://www.emorph.eu – Tacman, grant FP7-610967 – Poeticon, grant FP7-215843 http://www.poeticon.eu – Wysiwyd, grant FP7-612139 new – Walk-man, grant FP7-611832 projects • more information: – Koroibot, grant FP7-611909 http://www.iCub.org

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