# Generative grasp synthesis from demonstration using parametric mixtures

arXiv: https://arxiv.org/abs/1906.11548 · HTML: https://mlsanity.com/paper/1906.11548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [One-shot Learning for Autonomous Aerial Manipulation](https://mlsanity.com/paper/2206.01411/) (2022)
- [Robot Grasping and Manipulation: A Prospective](https://mlsanity.com/paper/2303.07807/) (2023)

## Abstract (arXiv)

We present a parametric formulation for learning generative models for grasp synthesis from a demonstration. We cast new light on this family of approaches, proposing a parametric formulation for grasp synthesis that is computationally faster compared to related work and indicates better grasp success rate performance in simulated experiments, showing a gain of at least 10% success rate (p < 0.05) in all the tested conditions. The proposed implementation is also able to incorporate arbitrary constraints for grasp ranking that may include task-specific constraints. Results are reported followed by a brief discussion on the merits of the proposed methods noted so far.