Data-Efficient Representation Learning for Grasping and Manipulation
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.