Erol Şahin KOVAN Araştırma Laboratuvarı Bilgisayar Mühendisliği Bölümü

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1 ODTÜ Bilgisayar Müh. KOVAN Araştırma Laboratuvarı’nda yapılan araştırmalar
Erol Şahin KOVAN Araştırma Laboratuvarı Bilgisayar Mühendisliği Bölümü Ortadoğu Teknik Üniversitesi Ankara

2 03/03/07 Sürü Aklı Sosyal böcekler – karıncalar, arılar, termitler – bireysel yeteneklerinin ötesindeki işleri ‘oğul’ olarak yapabiliyorlar. Büyük avları yuvaya taşıma İki nokta arasındaki en kısa yolu bulma Büyük yapılar inşa etme Bu sistemler çok sayıda etmenin yerel etkileşimlerinden ‘aklın’ nasıl ortaya çıkabileceğinin güzel örneklerini sunarlar. Bu etkileşimlerin ardında kendi- kendine örgütlenme (self- organization) yetisi yatar.

3 03/03/07 Robot Sürüleri Yakın gelecekte basit robotlar ucuza ve çok sayıda üretilebilecekler. Bu robotların, oğul olarak, istenen bir işi yerine getirebilmeleri için nasıl kontrol edilmeleri gerektiği ve hangi koordinasyon mekanizmalarını kullanabilecekleri sıcak bir araştırma konusudur. Çevresel bekçilik Felaket durumlarında otonom olarak iletişim altyapısı yaratılması Su şebekesi ya da gemi/uçak gibi büyük araçların devamlı ‘sağlık kontrolü’ Mayın tarama

4

5 Bilişsel Robotik * Courtesy of Francis Vachon

6 iCub insansı robotu Tabletop kinect Gaze mocap Human tracker kinect
Feature display World display Tabletop kinect Gaze mocap Human tracker kinect

7 Perception: Workspace detection
Snapshots from rviz ROS visualization tool. The perception system eciently detects and displays the objects on the table. iCub is represented by primitive shapes which becomes useful if the 3D points corresponding to the robot body is to be ltered out. From top-left to bottom right: Raw, Cluestered, Boxified, Identified point clouds are shown.

8 Experimental Setup: Behaviors
Push-left (PL) Push-right (PR) Push-forward (PF) Pull (PB) Top-grasp (TG) Side-grasp (SG)

9 Interaction Labeling round ball rolled
Nouns and adjectives as object labels Cup Box Cylinder Ball Short-long Thin-thick Edgy-round Verbs as effect labels moved-left (ML) moved-right (MR) moved-forward (MF) pulled (P) knocked-down (K) disappeared (D) no-change (NC)

10 Affordance prediction and Nouns
Figure shows online affordance prediction process and most probable effects that can be generated by applying certain behaviors on each object. Please note that these accuracy values've been improved considerably, a normalization bug detected recently. Percentage values are low, but a comparison between objects still yields reasonable results. The object on the right of the robot is a long cylinder, and the object on the left is a box-like object. Toppled effect, for instance, can be created much likely on the cylinder object (predicted as 50% for cylinder object, 29% for box-like object). Moreover, this cylinder object cannot be moved/toppled right because robot cannot push with its right backhand (we didn't allow the robot due to the damage risk on the electronics boards positioned on the backhand region). It is also shown that toppled left effect can be generated easily since robot is very good at pushing to the left with its right forehand. toppled effect contains toppled left effect since it can be in the directions that are not exactly left or right. Not only toppled effect but also moved backward/forward effect predictions are consistent with what we have expected. Both objects are almost at the same distance from the robot. Hence, they should be movable forward with similar accuracies (32% for cylinder, 31% for box-like objects). However, there is a considerable difference between moved backward effect predictions. The reason is cylinders couldn't be moved backward easily. They usually knocked down, toppled, or vanished from the table by rotating. Therefore, robot was not very much accurate while moving cylinder like objects backward. This result shows that what an object affords is not only about the features of the object, but also behavior repertoire of the agent that interacts with this object. No change effect is usually created by many behaviors (e.g. say pass me behavior when human cannot reach that object, or say hello behavior when human doesn't exist or he exists and sitting on the chair (interactable), or reach behavior when an object is not reachable). Since these type of instances are distinct, they are easily predicted and no change effect prediction is mostly the most accurate prediction all the time.

11 Multi-Step Planning

12 Human as a social entity
Human features : Gaze direction (roll, pitch, yaw angles) Body pose (x,y,z, roll, pitch, yaw angles) Human presence

13 Social Affordances: Reach the unreachable
“hello”

14 Daha çok bilgi @ http://kovan.ceng.metu.edu.tr/
Teşekkürler Baris Akgun, Ilkay Atil, Asil Bozcuoglu, Maya Cakmak,Yigit Caliskan, Nilgun Dag, Mehmet Dogar, Selda Eren, Sinan Kalkan, Erhan Oztop, Mustafa Parlaktuna, Doruk Tunaoglu, Gokturk Ucoluk,Emre Ugur, Kadir Uyanik, Onur Yuruten. This study is also partially funded by TUBITAK through Project 109E033. Daha çok