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

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 overwhelming inductive biases. Traditional robotic designs, while well-suited for their specific use cases, are often fragile when used with these algorithms as they fail to address the intermediate sub-optimal posterior-based behavior these algorithms exhibit. 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 features a semi-soft structure, a high degree of redundancy achieved through rich non-contact sensors (exclusively cameras), and strategically designed, 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 target-finding task even under simulated sensor failures, all with minimal human oversight during extended learning periods. Additional experiments on the robustness of the design show that it is able to withstand relatively large amounts of mechanical stress. We believe this design represents a concrete step toward more tailored robotic designs capable of supporting general-purpose, generally intelligent robots.