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20182022
most citedGenerative grasp synthesis from demonstration using parametric mixtures

4 citations · 10 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.RO20222 cited

BURG-Toolkit: Robot Grasping Experiments in Simulation and the Real World

Martin Rudorfer, Markus Suchi, Mohan Sridharan +2

This paper presents BURG-Toolkit, a set of open-source tools for Benchmarking and Understanding Robotic Grasping. Our tools allow researchers to: (1) create virtual scenes for gene…

cs.RO2022

Generating Task-specific Robotic Grasps

Mark Robson, Mohan Sridharan

This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation…

cs.RO20211 cited

Towards a Framework for Changing-Contact Robot Manipulation

Saif Sidhik, Mohan Sridharan, Dirk Ruiken

Many robot manipulation tasks require the robot to make and break contact with objects and surfaces. The dynamics of such changing-contact robot manipulation tasks are discontinuou…

cs.RO2021

Continual Learning of Knowledge Graph Embeddings

Angel Daruna, Mehul Gupta, Mohan Sridharan +1

In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications. Ho…

cs.RO20194 cited

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…