Ahmet Ercan Tekden — accepted AI-conference papers

2023

Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces

Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu

RA-L 2023

Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addresses the problem of transferring a grasp experience or a demonstration to a novel object that shares shape similarities

Cited by 10 Source ↗Scholar ↗

2023

Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands

Yiting Chen, Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu

IROS 2023poster

Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot's physical interaction with its environment. Acquiring accurate shape information about unknown objects is challenging, especially in unstructured environments, e.g. the vision sensors may only…

Cited by 12 Source ↗Scholar ↗