CAREER: Investigating the Ultimate Mechanisms of Embodied Cognition

到目前为止，在实现在非结构化环境(与工厂等结构化环境相比)中持续执行简单但自适应行为的机器方面取得的成功相对较少。制造这种机器的主流方法是复制在动物身上观察到的生理和神经系统，并将它们构建成机器人。然而，这引发了一个问题，即在现有的无限生物结构中，应该复制什么。该奖项的研究正在寻求另一种方法：不是复制现有的生物系统，而是在虚拟空间复制和连接进化动力学。由此产生的进化算法优化了虚拟机器人控制行为和身体规划的神经结构。重要的是，这些研究中的进化是特定于任务和行为的。这项研究旨在为机器人学和生物学做出重要贡献。对于机器人专家来说，这项工作将使计算机能够自动为机器人设计身体规划和神经控制器，这些机器人比人工设计的机器人更具适应性和健壮性。然后，自动设计的虚拟机器人可以被构建为物理设备，并部署到现实世界环境中，包括那些对人类构成危险的环境。对于生物学家来说，我们的研究将提供对自然界中特定结构为什么以及如何进化的洞察力。例如，如果最初为运动而进化的腿部机器人被选择来移动和抓取物体，那么计算进化可能会将机器人的前腿重新定位为手臂和抓手；或者，它可能会在现有的身体平面上增加操纵附件。这两种结果都会引起进化生物学家的极大兴趣。最后，实验将被安置在在线工具中，这些工具将允许研究生、本科生和K-12年级的学生在自己的机器上被动地运行进化模拟，并积极参与这个过程：他们可能会设计出新的虚拟环境，让机器人必须在其中进化。这种积极的参与旨在激励学生理解潜在的进化的物理、生物、工程和计算过程。

To date, relatively little success has been achieved in realizing machines that continually perform simple yet adaptive behaviors in unstructured environments (compared to a structured environment such as a factory). The prevailing approach to create such machines is to copy physiological and neurological systems observed in animals, and build them into robots. This raises the issue however of what from among the infinitude of existing biological structures should be copied. Research under this award is pursuing an alternative approach: rather than copy existing biological systems, evolutionary dynamics are copied and connected in a virtual space. The resulting evolutionary algorithm optimize virtual robots' neurological structures that control behavior and their body plans. Importantly, evolution in these studies is task and behavior specific.The research is intended to make important contributions to robotics and biology. For roboticists, this work will enable computers to automatically design the body plans and neural controllers for robots that are more adaptive and robust than robots designed manually. Automatically-designed virtual robots can then be built as physical devices and deployed into real-world environments, to include those that are dangerous to humans. For biologists, our studies will provide insight into why and how particular structures evolved in nature. For example, if legged robots originally evolved for locomotion are then selected to locomote and grasp objects, computational evolution may re-purpose the robot's front legs into arms and grippers; or, it may add manipulatory appendages onto the existing body plan. Either outcome would be of great interest to evolutionary biologists.Finally, experiments are being housed in online tools that will allow graduate, undergraduate and K-12 students to run evolutionary simulations passively on their own machines, as well as actively participate in the process: they may design novel virtual environments in which the robots must evolve. This active participation is intended to motivate students to understand the physics, biology, engineering and computational processes underlying evolution.