Haptic Knowledge Transfer Between Heterogeneous Robots using Kernel Manifold Alignment

Humans learn about object properties using multiple modes of perception. Recent advances show that robots can use non-visual sensory modalities (i.e., haptic and tactile sensory data) coupled with exploratory behaviors (i.e., grasping, lifting, pushing, dropping, etc.) for learning objects' properties such as shape, weight, material and affordances. However, non-visual sensory representations cannot be easily transferred from one robot to another, as different robots have different bodies and sensors. Therefore, each robot needs to learn its task-specific sensory models from scratch. To address this challenge, we propose a framework for knowledge transfer using kernel manifold alignment (KEMA) that enables source robots to transfer haptic knowledge about objects to a target robot. The idea behind our approach is to learn a common latent space from multiple robots' feature spaces produced by respective sensory data while interacting with objects. To test the method, we used a dataset in which 3 simulated robots interacted with 25 objects and showed that our framework speeds up haptic object recognition and allows novel object recognition.