Research Project Title:
Shape-Aware Representation Learning for Category-Level Manipulation Skills
abstract:Manipulation skill generalization across different object requires accounting for their variations, while still transferring relevant past experience. Even within a category, object shape can vary in complex, nonlinear ways, that are difficult to capture when adjusting an existing policy. We propose a new approach to skill transfer to novel objects that accounts for known categorical shape variation. A low-dimensional shape representation is learned from a set of deformations sampled between known objects within a category. This latent representation is mapped to the set of control parameters that result in successful execution of a category-level skill on that object. This method generalizes a learned manipulation policy to even complex unseen objects with few training examples.
My goal, through SuperUROP, is to make a positive contribution to the fields of robotics and embodied intelligence. I’m excited to apply what I’ve learned in my previous courses on AI, robotic manipulation, and machine learning towards this long-term research project.