The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals. To answer this question, we propose a mutual information–based representation of the relationships between the proprioceptive signals of a robot, which we call proprioceptive information graphs (π-graphs). Leveraging the fact that the information structure of the sensorimotor apparatus is dependent on the embodiment of the robot, we use the π-graph to look for pairwise signal relationships that reflect the underlying kinematic first-order principles applicable to the robot’s structure. In our discussion, we show that analysis of the π-graph leads to the inference of two fundamental elements of the robot morphology: its mechanical topology and corresponding kinematic description, that is, the location and orientation of the robot’s joints. Results from a robot manipulator, a hexapod, and a humanoid robot show that the correct topology and kinematic description can be effectively inferred from their π-graph either offline or online, regardless of the number of links and body configuration.

中文翻译：

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机器学习驱动的机器人身体形态的自我发现


通常假设机器人的形态是已知的，来自外部测量设备的数据主要用于其运动学校准。相比之下，我们采取以主体为中心的视角，思考一个模糊的探索问题：机器人是否可以依靠最少的先验知识并仅依赖于无组织的本体感受信号，自行学习其形态元素。为了回答这个问题，我们提出了一种基于互信息的机器人本体感受信号之间关系的表示，我们称之为本体感受信息图（π图）。利用感觉运动装置的信息结构取决于机器人的具体化这一事实，我们使用 π 图来寻找成对的信号关系，这些信号关系反映了适用于机器人结构的基本运动学一阶原理。在我们的讨论中，我们表明对 π 图的分析可以推断出机器人形态的两个基本元素：其机械拓扑和相应的运动学描述，即机器人关节的位置和方向。机器人操纵器、六足机器人和人形机器人的结果表明，无论连杆数量和身体配置如何，都可以通过离线或在线的 π 图有效地推断出正确的拓扑和运动学描述。