Generative grasp synthesis from demonstration using parametric mixtures

• DOI: 10.48550/arxiv.1906.11548
• Publication Date: 2019-01-01
• Authors: Ermano Arruda; Claudio Zito; Mohan Sridharan; Marek Kopicki; Jeremy Wyatt
• Keywords: Generative model

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