Using Relational Histogram Features and Action Labelled Data to Learn Preconditions for Means-End Actions

Severin Fichtl, Dirk Kraft, Norbert Krüger, Frank Guerin

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

The outcome of many complex manipulation ac-
tions is contingent on the spatial relationships among pairs of
objects, e.g. if an object is “inside” or “on top” of another.
Recognising these spatial relationships requires a vision system
which can extract appropriate features from the vision input
that capture and represent the spatial relationships in an easily
accessible way. We are interested in learning to predict the
success of “means end” actions that manipulate two objects at
once, from exploratory actions, and the observed sensorimo-
tor contingencies. In this paper, we use relational histogram
features and illustrate their effect on learning to predict a
variety of “means end” actions’ outcomes. The results show that
our vision features can make the learning problem significantly
easier, leading to increased learning rates and higher maximum
performance. This work is in particular important for robots
that need to reliably predict the success probability of their
multi object manipulating action repertoire in novel scenes.
OriginalsprogEngelsk
TitelIROS 2015 workshop proceedings
Antal sider6
ForlagIEEE
Publikationsdato2. okt. 2015
StatusUdgivet - 2. okt. 2015
Begivenhed2015 IEEE/RSJ International Conference on Intelligent Robots and Systems - Hamburg, Tyskland
Varighed: 28. sep. 20152. okt. 2015

Konference

Konference2015 IEEE/RSJ International Conference on Intelligent Robots and Systems
Land/OmrådeTyskland
ByHamburg
Periode28/09/201502/10/2015

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