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.

翻译：先进的机器学习算法需要极其鲁棒且配备丰富感官反馈的平台，以处理大量试错学习而不依赖于强归纳偏置。传统机器人设计虽适用于特定用例，但在配合此类算法时常显脆弱。为弥补这一差距——并受实现好奇心驱动的婴儿机器人愿景的启发——我们提出一种从头设计的新型机器人肢体。该设计采用软硬混合结构，具有高冗余度与丰富的非接触式传感器（仅使用摄像头），并包含易于更换的故障点。在物理样机上使用两种当代强化学习算法进行的原理验证实验表明，即使在模拟传感器故障条件下，该设计仍能成功完成简单的目标寻找任务，且在长时间学习过程中仅需极少人工监督。我们相信这一设计为实现通用、普适智能机器人迈出了坚实的一步，为定制化机器人设计提供了具体范例。