SenseNet: 3D Objects Database and Tactile Simulator

TL;DR

SenseNet is a comprehensive 3D object dataset and tactile simulation platform designed to advance AI research in sensorimotor and tactile feedback domains, complementing visual data-driven approaches.

Contribution

It introduces a large-scale tactile dataset and simulation environment to facilitate research in sensorimotor AI and tactile perception, filling a gap in existing datasets mainly focused on vision.

Findings

Provides a new resource for tactile and sensorimotor AI research

Enables training of AI systems with tactile feedback data

Fosters cross-disciplinary research in machine learning and neuroscience

Abstract

The majority of artificial intelligence research, as it relates from which to biological senses has been focused on vision. The recent explosion of machine learning and in particular, dee p learning, can be partially attributed to the release of high quality data sets for algorithm s from which to model the world on. Thus, most of these datasets are comprised of images. We believe that focusing on sensorimotor systems and tactile feedback will create algorithms that better mimic human intelligence. Here we present SenseNet: a collection of tactile simulators and a large scale dataset of 3D objects for manipulation. SenseNet was created for the purpose of researching and training Artificial Intelligences (AIs) to interact with the environment via sensorimotor neural systems and tactile feedback. We aim to accelerate that same explosion in image processing, but for the domain of tactile feedback and sensorimotor research. We hope that SenseNet can offer researchers in both the machine learning and computational neuroscience communities brand new opportunities and avenues to explore.