外观
论文arXiv 2608.216762 个月前
通过持久范畴建模实现统一任务驱动协同设计、验证与规划的机器人终身重组合
原文链接：Lifelong Robot Recomposition via Persistent Categorical Modeling for Unified Task-Driven Co-Design, Verification, and Planning（arXiv 2608.21676 · 发布日期：2026-08-21）
Lifelong Robot Recomposition via Persistent Categorical Modeling for Unified Task-Driven Co-Design, Verification, and Planning
针对设计后部署导致机器人对变化脆弱的问题，提出持久范畴组合框架与SMT求解，并在部署机器人和搜救场景验证。
核心要点
- 静态配置会把设计期假设固化为运行期约束，环境、任务或自身变化时易失效。
- 将机器人形式化为严格对称幺半范畴中的抽象电路，同步合成硬件、软件与行为。
- 框架支持Pareto前沿映射、不可行诊断、最小恢复和有限改动重配置等生命周期查询。
- 在部署机器人上对比多类官方规划器，并在搜救场景端到端演示；求解器和软件开源。
验证范围与局限：验证范围为部署机器人上的测量，以及一个搜救场景的端到端演示。
英文摘要 (Abstract)
Robotic systems are traditionally designed and deployed in static configurations, with assumptions made at design-time becoming immutable constraints during runtime. This design-then-deploy paradigm produces performant systems under narrow operating conditions, but renders robots brittle when qualities of themselves, their tasks, or their environments unexpectedly change. We address this challenge with a compositional framework that formalizes robotic systems as abstract circuits within a strict symmetric monoidal category, in which design and runtime composition of hardware, software, and behavior are synthesized simultaneously via an SMT-based solver, with monoidal functors projecting the system into lifecycle-specific views and free symbolic variables simultaneously solving for parameters and entire component specifications within larger compositions. This persistent model also supports queries a long-lived system needs beyond plan existence across its entire lifecycle, including mapping Pareto fronts over candidate compositions, diagnosing why a composition has become infeasible, finding its minimal restoration, and reconfiguring with limited change to the deployed system. We evaluate against official implementations of optimal numeric, stream-based, and SMT-based planners all measured onboard a deployed robot and demonstrate the approach end-to-end in a search-and-rescue scenario in which the robot recognizes when it has become unfit and synthesizes and assumes new holistic configurations to restore operation. We release our solver and supporting software open-source.
作者：Steven Swanbeck, Mitch Pryor
来源
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