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Automated Benchmarks and Optimization of Perception Tasks

Durner, Maximilian and Marton, Zoltan-Csaba and Kriegel, Simon and Brucker, Manuel and Riedel, Sebastian and Meinzer, Dominik and Triebel, Rudolph (2017) Automated Benchmarks and Optimization of Perception Tasks. IROS 2017: 2nd Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics, 28 September 2017, Vancouver, Canada.

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Abstract

Advanced robots operating in complex and dynamic environments require intelligent perception algorithms to navigate collision-free, analyze scenes, recognize relevant objects, and manipulate them. Nowadays, the perception of mobile manipulation systems often fails if the context changes due to a variation e.g. in the lightning conditions, the utilized objects, the manipulation area, or the environment. Then, a robotic expert is needed who needs to adjust the parameters of the perception algorithm and the utilized sensor or even select a better method or sensor. Thus, a high-level cognitive ability that is required for operating alongside humans is to continuously improving their performance based on introspection. This adaptability to changing situations requires different aspects of machine learning, e.g. storing experiences for life-long learning, creating annotated datasets for supervised learning through user interaction, Bayesian optimization to avoid brute-force search in high-dimensional data, and a unified representation of data and meta-data to facilitate knowledge transfer. Here we present how we automated and integrated different aspects of these.

Item URL in elib:https://elib.dlr.de/120239/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Automated Benchmarks and Optimization of Perception Tasks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Durner, Maximilianmaximilian.durner (at) dlr.dehttps://orcid.org/0000-0001-8885-5334
Marton, Zoltan-CsabaZoltan.Marton (at) dlr.dehttps://orcid.org/0000-0002-3035-493X
Kriegel, SimonSimon.Kriegel (at) dlr.dehttps://orcid.org/0000-0003-4711-8527
Brucker, ManuelManuel.Brucker (at) dlr.deUNSPECIFIED
Riedel, SebastianSebastian.Riedel (at) dlr.dehttps://orcid.org/0000-0002-3655-2486
Meinzer, Dominikdom.meinzer (at) gmail.comUNSPECIFIED
Triebel, RudolphRudolph.Triebel (at) dlr.deUNSPECIFIED
Date:19 August 2017
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Scene analysis, Bayesian optimization, Perception
Event Title:IROS 2017: 2nd Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics
Event Location:Vancouver, Canada
Event Type:Workshop
Event Dates:28 September 2017
Organizer:National Institute of Informatics, Japan
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Durner, Maximilian
Deposited On:11 Jun 2018 15:38
Last Modified:31 Jul 2019 20:17

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