面向先进机器学习算法、具备丰富交互能力的鲁棒柔性婴儿机器人
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
AI总结：
本文从零设计软硬混合、高冗余且仅依赖摄像头的可更换机器人肢体，验证其在传感器故障下仍可通过强化学习完成目标寻找。
摘要：
先进机器学习算法需要极其鲁棒且配备丰富感官反馈的平台，以便在不依赖强归纳偏置的情况下处理大量试错学习。传统机器人设计虽然非常适合其特定用例，但在与这些算法配合使用时往往较为脆弱。为弥补这一差距，并受实现好奇心驱动的婴儿机器人这一愿景启发，我们提出了一种从零开始设计的新型机器人肢体。该设计采用软硬混合结构，具有高冗余度和丰富的非接触式传感器（仅使用摄像头），并设有易于更换的失效点。在物理原型上使用两种当代强化学习算法开展的概念验证实验表明，即使在模拟传感器故障的情况下，我们的设计仍能成功完成简单的目标寻找任务，并且在较长学习周期内只需极少的人工监督。我们认为，该设计为实现面向通用、通用智能机器人的更具针对性的机器人设计迈出了具体一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
