115 citations · 128 across the 16 of their papers we have counts for
4 papers · 1 filter
Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning
Nathan F. Lepora, John Lloyd
This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model fo…
Goal-Driven Robotic Pushing Using Tactile and Proprioceptive Feedback
John Lloyd, Nathan F. Lepora
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 uncertain…
Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard
Alex Church, John Lloyd, Raia Hadsell +1
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…
Optimal Deep Learning for Robot Touch
Nathan F. Lepora, John Lloyd
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 con…