Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms

Autor:	Alhakami, Mohannad, Ashley, Dylan R., Dunham, Joel, Faccio, Francesco, Feron, Eric, Schmidhuber, Jürgen
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning I.2.9 I.2.6
Druh dokumentu:	Working Paper
Popis:	Artificial intelligence has made great strides in many areas lately, yet it has had comparatively little success in general-use robotics. We believe one of the reasons for this is the disconnect between traditional robotic design and the properties needed for open-ended, creativity-based AI systems. To that end, we, taking selective inspiration from nature, build a robust, partially soft robotic limb with a large action space, rich sensory data stream from multiple cameras, and the ability to connect with others to enhance the action space and data stream. As a proof of concept, we train two contemporary machine learning algorithms to perform a simple target-finding task. Altogether, we believe that this design serves as a first step to building a robot tailor-made for achieving artificial general intelligence. Comment: 5 pages in main text + 1 page of references, 7 figures in main text; source code available at https://github.com/dylanashley/robot-limb-testai
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.08093 Zobrazit plný text záznamu View this record from Arxiv