Goal-Driven Robotic Pushing Using Tactile and Proprioceptive Feedback

Autor: Nathan F. Lepora, John W. Lloyd

Publikováno v: IEEE Transactions on Robotics. 38:1201-1212

In robots, nonprehensile manipulation operations such as pushing are a useful way of moving large, heavy, or unwieldy objects, moving multiple objects at once, or reducing uncertainty in the location or pose of objects. In this study, we propose a re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4450d7afcf4c15c2b3639c7ccda4b783
https://doi.org/10.1109/tro.2021.3104471

Optimal Deep Learning for Robot Touch: Training Accurate Pose Models of 3D Surfaces and Edges

Autor: John W. Lloyd, Nathan F. Lepora

Publikováno v: IEEE Robotics & Automation Magazine. 27:66-77

This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::343ad777ef33332b8cf6d431c25c020c
https://doi.org/10.1109/mra.2020.2979658

Towards integrated tactile sensorimotor control in anthropomorphic soft robotic hands

Autor: Chris Ford, Andrew Stinchcombe, Alfred Brown, Manuel G. Catalano, Benjamin Ward-Cherrier, John W. Lloyd, Nathan F. Lepora, Matteo Bianchi

Publikováno v: ICRA
ICRA 2021
2021 IEEE International Conference on Robotics and Automation (ICRA)

In this work, we report on the integrated sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactil

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d41e963df4eb6545e098403865581a7e
https://doi.org/10.1109/icra48506.2021.9561350

Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard

Autor: Nathan F. Lepora, Alex Church, John W. Lloyd, Raia Hadsell

Artificial touch would seem well-suited for Reinforcement Learning (RL), since both paradigms rely on interaction with an environment. Here we propose a new environment and set of tasks to encourage development of tactile reinforcement learning: lear

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4ddf5428c6be9aa12698986c4224912

Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations

Autor: Luke Cramphorn, John W. Lloyd, Nathan F. Lepora

Publikováno v: ICRA
Cramphorn, L, Lloyd, J & Lepora, N 2018, Voronoi Features for Tactile Sensing : Direct Inference of Pressure, Shear, and Contact Locations . in 2018 IEEE International Conference on Robotics and Automation (ICRA 2018) : Proceedings of a meeting held 21-25 May 2018, Brisbane, Australia. . International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE) . https://doi.org/10.1109/ICRA.2018.8460644

There are a wide range of features that tactile contact provides, each with different aspects of information that can be used for object grasping, manipulation, and perception. In this paper inference of some key tactile features, tip displacement, c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d117679f5b290ffacdc8dd3590dfdce
https://doi.org/10.1109/icra.2018.8460644