Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Object and relation-centric reasoning of action effects in push manipulation tasks

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dc.contributor Graduate program in Computer Engineering.
dc.contributor.advisor Uğur, Emre.
dc.contributor.advisor Erdem, İbrahim Aykut.
dc.contributor.author Tekden, Ahmet Ercan.
dc.date.accessioned 2023-03-16T10:04:47Z
dc.date.available 2023-03-16T10:04:47Z
dc.date.issued 2020.
dc.identifier.other CMPE 2020 T45
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12435
dc.description.abstract In complex robotic systems, prediction of effects is a challenging problem when the number of objects varies, especially in the presence of rich and various interactions among these objects. To be able to model such systems, the representation of data should be able to su ciently encode multiple objects and interactions between them. In this thesis, we rst show our initial research on e ect prediction on objects with various shapes. Then we propose a Graph Neural Network based framework, Belief Regulated Dual Propagation Networks (BRDPN), a general-purpose learnable physics engine. Our framework consists of two complementary components, a physics predictor and a belief regulator. While the former predicts the future states of the object(s) manipulated by the robot, the latter constantly corrects the robot's knowledge regarding the objects and their relations. Through our experiments, in complex environments consisting of di erent shaped objects and articulation types we have shown that by using this framework, the robot can reliably predict the consequences of its actions in object trajectory level and exploit its own interaction experience to correct its belief about the state of the environment. Furthermore, we have shown that we can use this framework in tool manipulation and planning.
dc.format.extent 30 cm.
dc.publisher Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020.
dc.subject.lcsh Robotics.
dc.title Object and relation-centric reasoning of action effects in push manipulation tasks
dc.format.pages xvi, 75 leaves ;


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