activity
20172020
most citedBenchmarking In-Hand Manipulation

49 citations · 98 across the 4 of their papers we have counts for

collaborators

6 papers

cs.RO2020

Polyhedral Friction Cone Estimator for Object Manipulation

Morteza Azad, Silvia Cruciani, Michael J. Mathew +2

A polyhedral friction cone is a set of reaction wrenches that an object can experience whilst in contact with its environment. This polyhedron is a powerful tool to control an obje…

cs.RO202049 cited

Benchmarking In-Hand Manipulation

Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang +3

The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose…

cs.RO201913 cited

Dual-Arm In-Hand Manipulation and Regrasping Using Dexterous Manipulation Graphs

Silvia Cruciani, Kaiyu Hang, Christian Smith +1

This work focuses on the problem of in-hand manipulation and regrasping of objects with parallel grippers. We propose Dexterous Manipulation Graph (DMG) as a representation on whic…

cs.RO2018

Dexterous Manipulation Graphs

Silvia Cruciani, Christian Smith, Danica Kragic +1

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence…

cs.LG20171 cited

Unlocking the Potential of Simulators: Design with RL in Mind

Rika Antonova, Silvia Cruciani

Using Reinforcement Learning (RL) in simulation to construct policies useful in real life is challenging. This is often attributed to the sequential decision making aspect: inaccur…

cs.RO201735 cited

Reinforcement Learning for Pivoting Task

Rika Antonova, Silvia Cruciani, Christian Smith +1

In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successfu…