Robotic platforms that can efficiently collaborate with humans in physical tasks constitute a major goal in robotics. However, many existing robotic platforms are either designed for social interaction or industrial object manipulation tasks. The design of collaborative robots seldom emphasizes both their social interaction and physical collaboration abilities. To bridge this gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator. NICOL is a large, newly designed, scaled-up version of its well-evaluated predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and facial expression display and extends its manipulation abilities in terms of precision, object size, and workspace size. Our contribution in this paper is twofold -- firstly, we introduce the design concept for NICOL, and secondly, we provide an evaluation of NICOL's manipulation abilities by presenting a novel extension for an end-to-end hybrid neuro-genetic visuomotor learning approach adapted to NICOL's more complex kinematics. We show that the approach outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK and BIO-IK. Overall, this article presents for the first time the humanoid robot NICOL, and contributes to the integration of social robotics and neural visuomotor learning for humanoid robots.

翻译：能够与人类在物理任务中高效协作的机器人平台是机器人领域的主要目标之一。然而，许多现有机器人平台要么专为社交交互设计，要么专为工业物体操控任务设计。协作机器人的设计很少同时强调其社交交互与物理协作能力。为弥合这一差距，我们提出了新型半人形机器人NICOL（神经启发式协作机器人）。NICOL是其经过充分验证的前代神经启发式伴侣机器人NICO的放大升级版，采用了NICO的头部与面部表情显示模块，并在操控精度、物体尺寸及工作空间范围方面扩展了其操作能力。本文的贡献包含两方面：首先，我们介绍了NICOL的设计理念；其次，通过提出一种针对NICOL更复杂运动学特性改进的端到端混合神经遗传视觉运动学习方法的扩展方案，我们对NICOL的操控能力进行了评估。实验表明，该方法在性能上超越了当前最先进的逆运动学求解器KDL、TRACK-IK和BIO-IK。总的来说，本文首次展示了人形机器人NICOL，并为社交机器人与类人机器人神经视觉运动学习的融合做出了贡献。