# Modeling Grasp Motor Imagery through Deep Conditional Generative Models

## TL;DR

This paper explores how deep generative models can be used to enable robots to synthesize complex, multimodal grasp configurations by translating high-level motor imagery concepts into robotic grasp actions.

## Contribution

It introduces a novel approach using deep conditional generative models to learn integrated object-action representations for robotic grasp synthesis.

## Key findings

- Successfully generated multimodal grasp configurations in simulation
- Demonstrated the model's capacity to capture complex grasp variations
- Showed potential for applying deep generative models to robotic manipulation

## Abstract

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.