no code implementations • 21 Nov 2023 • Mauro Comi, Yijiong Lin, Alex Church, Alessio Tonioni, Laurence Aitchison, Nathan F. Lepora
To address these challenges, we propose TouchSDF, a Deep Learning approach for tactile 3D shape reconstruction that leverages the rich information provided by a vision-based tactile sensor and the expressivity of the implicit neural representation DeepSDF.
no code implementations • 26 Jul 2023 • Yijiong Lin, Mauro Comi, Alex Church, Dandan Zhang, Nathan F. Lepora
To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision.
no code implementations • 26 Aug 2022 • Anupam K. Gupta, Alex Church, Nathan F. Lepora
The sense of touch is fundamental to human dexterity.
no code implementations • 8 Sep 2021 • Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora
In addition, the unsheared tactile images give a faithful reconstruction of the contact geometry that is not possible from the sheared data, and robust estimation of the contact pose that can be used for servo control sliding around various 2D shapes.
2 code implementations • 16 Jun 2021 • Alex Church, John Lloyd, Raia Hadsell, Nathan F. Lepora
Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs.
no code implementations • 5 Feb 2021 • Nathan F. Lepora, Andrew Stinchcombe, Chris Ford, Alfred Brown, John Lloyd, Manuel G. Catalano, Matteo Bianchi, Benjamin Ward-Cherrier
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 tactile sensor based on the human sense of touch.
Robotics
no code implementations • 3 Dec 2020 • John Lloyd, Nathan F. Lepora
We evaluate our method by pushing objects across planar and curved surfaces.
Robotics
no code implementations • 27 Oct 2020 • Anupam K. Gupta, Andrei Nakagawa, Nathan F. Lepora, Nitish V. Thakor
With the increase in interest in deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important.
1 code implementation • 6 Aug 2020 • Alex Church, John Lloyd, Raia Hadsell, Nathan F. Lepora
Artificial touch would seem well-suited for Reinforcement Learning (RL), since both paradigms rely on interaction with an environment.
no code implementations • NeurIPS 2016 • Nathan F. Lepora
Decision making under uncertainty is commonly modelled as a process of competitive stochastic evidence accumulation to threshold (the drift-diffusion model).