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Large-scale Incremental Machine Learning for Robotics
2nd Brains, Minds and Machines Summer School 2015/08/17
Raffaello Camoriano, PhD Fellow @IIT (iCub Facility) - UNIGE (DIBRIS)
Advisors: Giorgio Metta, Lorenzo Rosasco
Img credits: C
. Ciliberto
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Modern robots are expected to operate in unstructured environments...
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...and to learn new tasks on the fly...
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...across different modalities...
Vision
Tactile sensing
Force sensing
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...throughout potentially very long time spans.
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Machine Learning provides a number of useful tools for facing these challenges, e.g.:
• Large-scale learning methods
• Incremental Learning
• Transfer Learning
Machine Learning to the Rescue!
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• Machine Learning
– Large-scale Learning – Incremental Learning – Memory/time/accuracy tradeoff
• Robotics applications (on iCub)
– Incremental object recognition
– Dynamical system identification & estimation
A. Rudi
F.Nori S.Traversaro
G. Pasquale C. Ciliberto A. Rudi
With
My Main Focuses
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Nyström Computational Regularization
• How to deal with a large number of
examples n?
• Nyström method – Effective dimensionality < n – Approximate K drawing m << n
points • Memory: O(n2) → O(mn), • Time: O(n3) → O(m2 n + m3)
• A. Rudi, R. C., L. Rosasco, "Less is
More: Nyström Computational Regularization", NIPS 2015 (accepted) http://arxiv.org/abs/1507.04717
• Nyström approximation as a regularization operation
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Nyström Computational Regularization
• Naive Nystrom KRLS: Compute a kernel approximation with a large m and then regularize with λ.
• Waste of resources (time & memory).
• Our variant of Nyström KRLS:
• Regularization can be viewed as "discarding" irrelevant eigencomponents
• m: trade-off parameter controlling both computational complexity and regularization
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Incremental Object Recognition
• Scenario:
– iCub detects an unknown object, which it cannot recognize with sufficient
confidence
– It shall be able to update its object recognition model adding a new class, without retraining from scratch (unsustainable in the lifelong learning setting)
– This can be done, e.g. by means of a slight extension of RLS and proper reweighting
– Open question: How to change the amount of regularization efficiently as n grows in time?
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System Identification and Estimation
• Instance of incremental learning in the robotic motion domain • Semiparametric inverse dynamics estimation and inertial parameters
identification • [ f ; τ ] = ID( q , dq/dt ,d2q/dt2 ) Multiple output regression problem
– IN: q, dq/dt ,d2q/dt2 for the first 4 arm joints (shoulder + elbow) – OUT: Wrench (generalized force) measured by the F/T sensor placed in the arm
• Challenges:
– Interpretability (join together physics-based parametric modeling and "black-box" nonparametrics)
– Accuracy (beyond rigid body dynamics models, e.g. flexible bodies) – Adaptivity (the model adapts incrementally to change in system or environmental
properties)
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Source: Camoriano, Raffaello, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, and
Francesco Nori. "Incremental semiparametric inverse dynamics learning." In Robotics andAutomation (ICRA), 2016 IEEE International Conference on, pp. 544-550. IEEE, 2016.
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System Identification and Estimation
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For more information, see https://ocw.mit.edu/help/faq-fair-use/.
Source: Camoriano, Raffaello, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, and
Francesco Nori. "Incremental semiparametric inverse dynamics learning." In Robotics and
Automation (ICRA), 2016 IEEE International Conference on, pp. 544-550. IEEE, 2016.
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The GURLS Library
• Grand Unified Regularized Least Squares • Complete library for linear and kernel RLS • Supports large-scale datasets • MATLAB and C++ APIs • https://github.com/LCSL/GURLS • Maintainers:
– Alessandro Rudi – Matteo Santoro – Andrea Schiappacasse – Me
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