3 citations · 9 across the 5 of their papers we have counts for
5 papers · 1 filter
Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction
Kiyanoush Nazari, Gabriele Gandolfi, Zeynab Talebpour +3
This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on…
Proactive slip control by learned slip model and trajectory adaptation
Kiyanoush Nazari, Willow Mandil, Amir Ghalamzan E
This paper presents a novel control approach to dealing with object slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipu…
Action Conditioned Tactile Prediction: case study on slip prediction
Willow Mandil, Kiyanoush Nazari, Amir Ghalamzan E
Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, availa…
Deep Movement Primitives: toward Breast Cancer Examination Robot
Oluwatoyin Sanni, Giorgio Bonvicini, Muhammad Arshad Khan +3
Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldw…
A data-set of piercing needle through deformable objects for Deep Learning from Demonstrations
Hamidreza Hashempour, Kiyanoush Nazari, Fangxun Zhong +1
Many robotic tasks are still teleoperated since automating them is very time consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce programming time and cost…