We proposed a method for learning the actual body image of a musculoskeletal humanoid for posture generation and object manipulation using inverse kinematics with redundancy in the shoulder complex. The effectiveness of this method was confirmed by realizing automobile steering wheel operation. The shoulder complex has a scapula that glides over the rib cage and an open spherical joint, and is supported by numerous muscle groups, enabling a wide range of motion. As a development of the human mimetic shoulder complex, we have increased the muscle redundancy by implementing deep muscles and stabilize the joint drive. As a posture generation method to utilize the joint redundancy of the shoulder complex, we consider inverse kinematics based on the scapular drive strategy suggested by the scapulohumeral rhythm of the human body. In order to control a complex robot imitating a human body, it is essential to learn its own body image, but it is difficult to know its own state accurately due to its deformation which is difficult to measure. To solve this problem, we developed a method to acquire a self-body image that can be updated appropriately by recognizing the hand position relative to an object for the purpose of object manipulation. We apply the above methods to a full-body musculoskeletal humanoid, Kengoro, and confirm its effectiveness by conducting an experiment to operate a car steering wheel, which requires the appropriate use of both arms.

翻译：我们提出了一种方法，用于通过利用肩部复合体冗余度的逆运动学，学习肌肉骨骼人形机器人的实际身体像，以进行姿态生成和物体操控。通过实现汽车方向盘操作，验证了该方法的有效性。肩部复合体包含可在胸腔上滑动的肩胛骨和一个开放式球窝关节，并由众多肌群支撑，从而实现了大范围的运动。作为人体仿生肩部复合体的进一步发展，我们通过实现深层肌肉增加了肌肉冗余度，并稳定了关节驱动。为了利用肩部复合体的关节冗余度进行姿态生成，我们考虑了基于人体肩肱节律所提示的肩胛驱动策略的逆运动学。为了控制模仿人体的复杂机器人，学习其自身的身体像至关重要，但由于其难以测量的形变，准确了解自身状态十分困难。为解决此问题，我们开发了一种自身体像获取方法，该方法可通过识别手部相对于物体的位置（以进行物体操控为目的）进行适当更新。我们将上述方法应用于全身肌肉骨骼人形机器人Kengoro，并通过进行一项需要双臂协调操作的汽车方向盘操控实验，验证了其有效性。