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Amir M. Ghalamzan E.

5 accepted papers

2020

Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

IROS 2020poster

Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous anal…

Cited by 15SourceScholar
2019

Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery

IROS 2019poster

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance de…

Cited by 55SourceScholar
2016

Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

IROS 2016poster

This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems ar…

Cited by 25SourceScholar
2015

An incremental approach to learning generalizable robot tasks from human demonstration

ICRA 2015poster

Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstratio…

Cited by 56SourceScholar