4 accepted papers
A holistic understanding of object properties across diverse sensory modalities (e.g., visual, audio, and haptic) is essential for tasks ranging from object categorization to complex manipulation. Drawing inspiration from cognitive science studies that emphasize the significance of multi-sensory int…
Humans leverage multiple sensor modalities when interacting with objects and discovering their intrinsic properties. Using the visual modality alone is insufficient for deriving intuition behind object properties (e.g., which of two boxes is heavier), making it essential to consider non-visual modal…
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' properti…
When identifying an object and its properties, humans use features from multiple sensory modalities produced when manipulating the object. Motivated by this cognitive process, we propose a deep learning methodology for object category recognition which uses visual, auditory, and haptic sensory data…