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Robotic platforms that can efficiently collaborate with humans in physicaltasks constitute a major goal in robotics. However, many existing roboticplatforms are either designed for social interaction or industrial objectmanipulation tasks. The design of collaborative robots seldom emphasizes boththeir social interaction and physical collaboration abilities. To bridge thisgap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator.NICOL is a large, newly designed, scaled-up version of its well-evaluatedpredecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head andfacial expression display and extends its manipulation abilities in terms ofprecision, object size, and workspace size. Our contribution in this paper istwofold -- firstly, we introduce the design concept for NICOL, and secondly, weprovide an evaluation of NICOL's manipulation abilities by presenting a novelextension for an end-to-end hybrid neuro-genetic visuomotor learning approachadapted to NICOL's more complex kinematics. We show that the approachoutperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IKand BIO-IK. Overall, this article presents for the first time the humanoidrobot NICOL, and contributes to the integration of social robotics and neuralvisuomotor learning for humanoid robots.

NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation