89 citations · 156 across the 22 of their papers we have counts for
9 papers · 1 filter
Multisensory Learning Framework for Robot Drumming
A. Barsky, C. Zito, H. Mori +2
The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for col…
Robust and fast generation of top and side grasps for unknown objects
Brice Denoun, Beatriz Leon, Claudio Zito +3
In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camer…
2D Linear Time-Variant Controller for Human's Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes
Claudio Zito, Tomasz Deregowski, Rustam Stolkin
Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human op…
Automatic Detection of Myocontrol Failures Based upon Situational Context Information
Karoline Heiwolt, Claudio Zito, Markus Nowak +2
Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat…
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…
Metrics and Benchmarks for Remote Shared Controllers in Industrial Applications
Claudio Zito, Maxime Adjigble, Brice D. Denoun +3
Remote manipulation is emerging as one of the key robotics tasks needed in extreme environments. Several researchers have investigated how to add AI components into shared controll…
Let's Push Things Forward: A Survey on Robot Pushing
Jochen Stüber, Claudio Zito, Rustam Stolkin
As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable cir…
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.…