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

• URL: http://arxiv.org/abs/2404.08093v2
• Date: Wed, 04 Dec 2024 14:45:23 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, Yanning Dai, Francesco Faccio, Eric Feron, Jürgen Schmidhuber,
• Abstract summary: We present a novel robotic limb designed from scratch to handle advanced machine learning algorithms. Our design has a hybrid soft-hard structure, high redundancy with rich non-contact sensors (exclusively cameras) and easily replaceable failure points. We believe this design represents a concrete step toward more tailored robotic designs for achieving general-purpose, generally intelligent robots.
• Score: 24.623734020960633
• License:
• Abstract: Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on strong inductive biases. Traditional robotic designs, while well-suited for their specific use cases, are often fragile when used with these algorithms. To address this gap -- and inspired by the vision of enabling curiosity-driven baby robots -- we present a novel robotic limb designed from scratch. Our design has a hybrid soft-hard structure, high redundancy with rich non-contact sensors (exclusively cameras), and easily replaceable failure points. Proof-of-concept experiments using two contemporary reinforcement learning algorithms on a physical prototype demonstrate that our design is able to succeed in a simple target-finding task even under simulated sensor failures, all with minimal human oversight during extended learning periods. We believe this design represents a concrete step toward more tailored robotic designs for achieving general-purpose, generally intelligent robots.

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