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

• URL: http://arxiv.org/abs/2404.08093v1
• Date: Thu, 11 Apr 2024 19:15:45 GMT
• Title: Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
• Authors: Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber,
• Abstract summary: We build a robust, partially soft robotic limb with a large action space and rich sensory data stream from multiple cameras. As a proof of concept, we train two contemporary machine learning algorithms to perform a simple target-finding task.
• Score: 25.85850890842541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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