重组机器人学：超越形态的跨域、开放集与终身模块化
Recompositional Robotics: Cross-Domain, Open-set, and Lifelong Modularity Beyond Morphology
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出重组机器人学，旨在通过跨硬件、软件、计算与行为的异构模块重组，最大化集成跨度并最小化集成惯性，以提升机器人基本能力的适应性，并定义问题、结合实例提出开放问题。
AI中文摘要：
模块化机器人领域的研究已产生了多种能力强大的方法，允许机器人的形态在线改变，近期的研究还致力于开发方法以决定采用何种形态，并自动将该决策传播到机器人的运动规划与控制中。这些方法功能强大，并增强了机器人在实际环境中的适应性。然而，另一种目标并非构建结构可变的机器人，而是构建其基本能力可变的机器人，其中能力是多个领域（包括运动动力学、感知、计算以及高层协调行为）的联合函数。设计为可在这些领域间重组的机器人，其改变能力的能力要强于仅在单一领域内可重组的机器人。我们将这种跨域可重组性称为集成跨度，并认识到一种对重组阻力的互补度量，称之为集成惯性。当前的模块化机器人在结构领域降低了集成惯性，但在其他有助于集成跨度的领域中，惯性仍然很高。我们断言，在实践中通过重组能提供最大效用的系统是那些最大化跨度并最小化惯性的系统，并将这一普遍问题称为重组机器人学：对包括硬件、软件、计算和行为在内的异构模块集进行适应，通过每个组件所需和提供的接口对其进行抽象，从而能够整体地进行推理。我们定义该问题，将其扎根于两个已部署系统和活跃的研究工作中，并就重组机器人学的未来提出开放性问题。
英文摘要：
Research in modular robotics has produced capable approaches allowing a robot's morphology to change online, with recent efforts also developing approaches to decide which morphology to assume and automatically propagate that decision into the robot's motion planning and control. These approaches are powerful and increase adaptability in the field. However, an alternative objective is not to build robots whose structures can change, but robots whose fundamental capabilities can change, where capability is a joint function across several domains, including kino-dynamics, perception, compute, and high-level coordinating behaviors. A robot designed to be reconfigured across these domains has a greater capacity to alter its capability than one that can be reconfigured in a single domain. We refer to this cross-domain reconfigurability as integration span, and recognize a complementary measure of the resistance to reconfiguration, which we refer to as integration inertia. Current modular robots have reduced integration inertia in the structural domain while it remains high in the other domains that contribute to integration span. We assert that the systems that can provide the most utility through reconfiguration in practice are those maximizing span and minimizing inertia and call this general problem recompositional robotics: adaptation over a heterogeneous set of modules including hardware, software, compute, and behavior that abstracts each component by the interfaces it requires and provides such that they can be reasoned over holistically. We define the problem, ground it in two deployed systems and active research efforts, and pose open questions about the future of recompositional robotics.
