40 citations · 120 across the 9 of their papers we have counts for
5 papers · 1 filter
Deep Dexterous Grasping of Novel Objects from a Single View
Umit Rusen Aktas, Chao Zhao, Marek Kopicki +2
Dexterous grasping of a novel object given a single view is an open problem. This paper makes several contributions to its solution. First, we present a simulator for generating an…
Learning better generative models for dexterous, single-view grasping of novel objects
Marek Kopicki, Dominik Belter, Jeremy L. Wyatt
This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point. Recently, progress has been m…
Generative grasp synthesis from demonstration using parametric mixtures
Ermano Arruda, Claudio Zito, Mohan Sridharan +2
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…
Feature-Based Transfer Learning for Robotic Push Manipulation
Jochen Stüber, Marek Kopicki, Claudio Zito
This paper presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends our previous work on contact-based predict…
Hypothesis-based Belief Planning for Dexterous Grasping
Claudio Zito, Valerio Ortenzi, Maxime Adjigble +3
Belief space planning is a viable alternative to formalise partially observable control problems and, in the recent years, its application to robot manipulation problems has grown.…