Ahmet Tekden

Robotic manipulation requires models that can generalize across variations in objects, scenes, and task conditions. However, collecting large-scale datasets that capture such variations in real-world robotic settings remains costly and time-consuming, making data-efficient learning an important challenge. This thesis investigates how the choice of representation can influence data-efficient generalization in robotic grasping and manipulation. First, we introduce local shape descriptors that allow grasp poses to transfer across object categories by exploiting shared geometric structure. Second, we develop neural field models that represent scenes and motions as smooth functions of latent variables learned from demonstrations. This formulation organizes demonstrations in a structured latent space, enabling motion generation from a small number of demonstrations and generalization across scene variations through interpolation. Third, we propose 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 state dependent phase formulation further enables the representation of complex motion patterns while preserving reactivity. Together, these approaches demonstrate how representation choices can improve data efficiency and generalization in robotic grasping and manipulation.

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 with objects the robot has previously encountered. Existing approaches for solving this problem are typically restricted to a specific object category or a parametric shape. Our approach, however, can transfer grasps associated with implicit models of local surfaces shared across object categories. Specifically, we employ a single expert grasp demonstration to learn an implicit local surface representation model from a small dataset of object meshes. At inference time, this model is used to transfer grasps to novel objects by identifying the most geometrically similar surfaces to the one on which the expert grasp is demonstrated. Our model is trained entirely in simulation and is evaluated on simulated and real-world objects that are not seen during training. Evaluations indicate that grasp transfer to unseen object categories using this approach can be successfully performed both in simulation and real-world experiments. The simulation results also show that the proposed approach leads to better spatial precision and grasp accuracy compared to a baseline approach.