Modeling Grasp Motor Imagery through Deep Conditional Generative Models
This work addresses the challenge of equipping machines with human-like grasping capabilities, which is incremental as it applies existing deep learning techniques to a specific robotic domain.
The paper tackled the problem of robotic grasp synthesis by translating high-level motor imagery concepts using deep learning, and demonstrated the capacity of generative models to capture and generate multimodal, multi-finger grasp configurations on a simulated dataset.
Grasping is a complex process involving knowledge of the object, the surroundings, and of oneself. While humans are able to integrate and process all of the sensory information required for performing this task, equipping machines with this capability is an extremely challenging endeavor. In this paper, we investigate how deep learning techniques can allow us to translate high-level concepts such as motor imagery to the problem of robotic grasp synthesis. We explore a paradigm based on generative models for learning integrated object-action representations, and demonstrate its capacity for capturing and generating multimodal, multi-finger grasp configurations on a simulated grasping dataset.