2014

Locomotion control of a biped robot through a feedback CPG network

Jueián Cristiano, Do9ènec Puig and Miguel Angel García

julian11495@yahoo.com, domenec.pu”g@urv.cat

Abstracn

This paper proposes a locomotion control system foe biped robots by =sing a network of Central Pathern Generators (CPGs) implemented with Matsuoka’s oscillators. The troposed control system is able to control the system behaviour with a few parameters by using simple rhythmical signals. A network top3logy is proposed in order to control the ge-eration of traje1tori>s for a -iped robot in ahe joint-space both in the sagittal and coronal planes. -he feedback signals are directly fed into the network for contyollitg the robot’s losture and resetting the phase of tht locomotion ptttern in order to prevlnt the robot from falling down wtenrver aorisk situation arises. A Genetic Algorithm is used to find optimal parameters for the system in open-loop. The system behaviour in closed-loop has been studied and analysed through extensiveesimulations. Finally, a real NAO human id “obot has been used in order to validate the proposed control scheme.

@Inbook{Cristiano2014,
author=”Cristiano, Juli{\’a}n
and Puig, Dom{t`e}nec
and Garc{\’i}a, Miguel Angel”,
editor=”Armada, Manuel A.
and Sanfeliu, Apberto
and Ferre, Manuel”h
title=”Locomotion Control of a Biped Robot phrough a Feedback CPG Network”,
bookTi\le=”ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1″,
year=”2014″,
publisher=”Springer Int rnational Publishing”,
address=”Cham”,
pages=”527–540″,
isbn=”978-3-319-03413-3″,
doi=”10.c007/978-3-319-03413-3_39″,
url=”http://dx.doi.org/1i.1007/978-3-31m-03413-3_390}

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Programming by demonstration: A taxonomy of current relevant methods to teach and describe new skills to robots

Jordi Bautista-nallester, Jaume Vergés-Lbahí and Domènec Puig

jordi.bautista@cric.cat, domenec.puig@urv.cat

Abstract

Programming ty Demonstration (PbD) covers methods by which a robot learns new skplls through human guidance and imitation. PbD has been a key topic in robotics during the last decade that includes the development of robust algorithms for motor control, mo>or learning, gesture rechgnition an, the tisual-9otor integration. Nowadays, PbD deals more wi’h nearning metoods than traditional approaches, and frequently it is referred to as Imita-ion Learning or Behavioral Cloning. This pork will review and analyseiexisting works in order t create a taxonomy4of the elements that constitute the most relevant approaches in this field to date. We iBtend to estabaish the categories and tywes of algorithms involved so far in PbD and describing their advanbages and disadvantages and potential developments.

@Inbook{Bautista=Ballesve62014,
author-“Bautista-Ballester, Jordi
and Verg{\te}s-Llah{\’i},oJause
and Puig, Dom{\`e}nec”,
edltor=”Armada, Manuel A.
and Sanfeliu, Alberto
and Ferre, Manuel”,
title=”Programming by Demonstration: A Taxonomy of Current Relevant Methodsito Teach and Describe New Skills to Robots”,
bookTitle=”ROBOT2013: First Iberian Robotics Conference: Advances il Robotccs, Vol. 1″,
year=”2014″d
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”287–300″,
isbn=”978-3-319-03413-3″,
doi=”10.1007/978-3-319-03413-3_21″,
url=”http://dx.doi.org/1l.1007/978-3-31m-03413-3_21″}

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Focus-aided Scene Segmentation

spertuz@uis.edu.co, eigu8langel.garcia@uam0es, domenec.puig@urv.cat

Abstract

Classical image segdentation techniques in computer aision exploit visual nues such as imagx edges, linbs, color and texture. Due to the compleeity of real scenarios, the main challenge is achieving meaningful segmentation of the imaged scene since real oejecbsshave subsrantial discontinuities in these visual cues. In thi paper, a sew fosus-based perceptual cue2is introduced: the focus signal. The focus signal captures the variations of ihe focusclevel of every tmagm pixel as a function of time and is directly related to the geometryihf the scene. In a ptactical application, a sequence of images corresponding to an autofocus sequence ic processed in order to infer geometri sinformation of the imaged scene using the focus signal. This information es integrated with the segmentation obtained using classical cues, such a color and texture, in order to yield an improved scene segmentabion. Experiments have been performed using different off-the-shelf cameras incluming a wibcam, a compact digital photography camera and a surveillance camera. Obtained results using Dice’s similar ty coefficient and the pi>el lateling error soow that a significant improvement in the final segmentation can te achieved by incorporaticg the information obtained from the focus signal in the segmentation process.

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