Skip to content
Log in

Author

A. Tekden

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Data-Efficient Representation Learning for Grasping and Manipulation

This thesis introduces local shape descriptors that allow grasp poses to transfer across object categories by exploiting shared geometric structure and proposes a potential-function-based framework for reactive motion generation, where neural fields model smooth energy functions whose gradients generate well-behaved vector fields for control.

A. Tekden · 0 citations
Open access Jul 2026

Compositional Motion Generation From Demonstration With Object-Centric Neural Fields

This work proposes a generative learning-from-demonstration framework that enables compositional modeling of robotic behavior by connecting perception and motion through shared object-level representations, and renders scenes from object-centric neural representations that integrate canonical neural fields with latent-conditioned deformations.

A. Tekden, Yasemin Bekiroglu · 0 citations