发表机构
Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出层次优化HOT，从零联合设计工具结构、形状与动作，仅评估少量结构即发现功能工具，并在真实机器人上验证。
AI 中文摘要
为任务设计工具的能力标志着一种超越仅仅理解、选择或使用工具的智能水平。现有的机器人工具设计方法通常在一个预先规定或预先生成的结构内优化工具的连续形状和动作，因此结构本身游离于物理优化循环之外。我们研究从零开始的任务驱动工具设计，其中工具的结构、形状和动作均源自期望的物理结果。我们表明，这三个要素可以通过HOT（层次优化）联合设计，其上层使用BASS搜索离散的工具结构，而下层的物理优化评估其任务行为，并将里程碑进展作为行为证据返回给搜索，最终提供联合优化的形状和动作。在四个具有不同物理功能的工具使用任务中，HOT在仅评估包含多达5600万个结构的搜索空间的一小部分后，便发现了功能结构，随后对其几何形状的细化在所有任务上降低了任务损失，同时保持了成功率，且变形在功能上可解释。一旦3D打印出来，这些工具能够在真实机器人上完成所有任务，动作在仿真中找到。从所需的物理效果而非已知工具目录出发设计工具，是迈向人类和动物中看到的开放式工具制造的一步。
英文摘要
The ability to design a tool for a task marks a level of intelligence beyond merely understanding, selecting, or using one. Existing methods for robotic tool design typically optimize a tool's continuous shape and action within a structure that is prescribed or generated beforehand, so the structure itself stays outside the physical optimization loop. We study task-driven tool design from scratch, where tool structure, shape, and action are all derived from the desired physical outcome. Here we show that the three elements can be designed jointly by HOT, a hierarchical optimization whose upper level searches over discrete tool structures with BASS, while lower-level physical optimization evaluates their task behavior and returns milestone progress as behavioral evidence for the search, ultimately providing jointly optimized shape and action. On four tool-use tasks with distinct physical functions, HOT discovers functional structures after evaluating only a small fraction of search spaces containing up to 56 million structures, and the subsequent refinement of their geometry lowers the task loss on all tasks while preserving success, through deformations that are functionally interpretable. Once 3D printed, the tools accomplish all tasks on a real robot with the actions found in simulation. Designing tools from required physical effects, rather than a catalog of known tools, is a step toward the open-ended tool making seen in humans and animals.
