Internal computational models of physical bodies are fundamental to the ability of robots and animals alike to plan and control their actions. These "self-models" allow robots to consider outcomes of multiple possible future actions, without trying them out in physical reality. Recent progress in fully data-driven self-modeling has enabled machines to learn their own forward kinematics directly from task-agnostic interaction data. However, forward-kinema\-tics models can only predict limited aspects of the morphology, such as the position of end effectors or velocity of joints and masses. A key challenge is to model the entire morphology and kinematics, without prior knowledge of what aspects of the morphology will be relevant to future tasks. Here, we propose that instead of directly modeling forward-kinematics, a more useful form of self-modeling is one that could answer space occupancy queries, conditioned on the robot's state. Such query-driven self models are continuous in the spatial domain, memory efficient, fully differentiable and kinematic aware. In physical experiments, we demonstrate how a visual self-model is accurate to about one percent of the workspace, enabling the robot to perform various motion planning and control tasks. Visual self-modeling can also allow the robot to detect, localize and recover from real-world damage, leading to improved machine resiliency. Our project website is at: https://robot-morphology.cs.columbia.edu/
翻译:物理机体的内部计算模型对于机器人和动物规划和控制其行动的能力来说都是根本性的。 这些“ 自我模型” 使机器人能够考虑未来多种可能行动的结果, 而不在物理现实中尝试。 完全数据驱动的自我模型最近的进展使机器能够直接从任务- 不可知互动数据中学习自己的远前运动体型。 然而, 前脑部/ 结构模型只能预测形态学的有限方面, 如终端效应器的位置或联合和大众的速度。 一个关键的挑战是如何模拟整个形态和运动学, 而不事先知道形态学的哪些方面与未来的任务相关。 我们在这里建议, 与其直接建模前皮肤模型, 一种更有用的自我模型形式可以回答空间占用询问, 以机器人的状态为条件。 这种由查询驱动的自我模型在空间域、 记忆力效率、 完全差异化和运动意识方面是连续的。 在物理实验中, 我们展示了视觉自我模型如何将形态学的哪些方面与未来的任务相关。 我们提议, 直接建模前的自我模型是直接建模, 使真实的机器人的模型能够测量到一个机器人的轨道 。