ManiMorph: Object Representations in Robot Manipulators Morphology for Improving Multi-Task Manipulation Performance

Abstract

Robot manipulation tasks involve direct interactions with objects, which can be viewed as dynamic changes to the robot’s kinematic chain. Morphology-aware learning frameworks, in which robot embodiment is explicitly modeled, do not account for these object-induced changes in their architectures. We address this gap by proposing ManiMorph, a multi-task, morphology-aware manipulation-learning framework in which object features are integrated into the robot’s morphological graph. We demonstrate that this node-centric representation, combined with a Feature-wise Linear Modulation (FiLM) task component, enhances the performance of the morphology-aware frameworks for robotic manipulation and generalizes effectively to new object variations.