# Modeling Grasp Motor Imagery through Deep Conditional Generative Models

arXiv: https://arxiv.org/abs/1701.03041 · HTML: https://mlsanity.com/paper/1701.03041/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [GKNet: grasp keypoint network for grasp candidates detection](https://mlsanity.com/paper/2106.08497/) (2021)
- [Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch](https://mlsanity.com/paper/2212.08604/) (2022)
- [DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning](https://mlsanity.com/paper/2205.05218/) (2022)

## Abstract (arXiv)

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.