NORA: Neural Object Representation for Autonomous Manipulation

摘要

We propose NORA, a neural object representation learning method designed for autonomous manipulation tasks. NORA learns rich, generalizable object representations that capture both geometric and semantic properties of objects, enabling robots to reason about object affordances and plan manipulation strategies in novel environments with improved generalization to unseen objects.