Lifelong Robot Recomposition via Persistent Categorical Modeling for Unified Task-Driven Co-Design, Verification, and Planning

2026-08-21

AI Summary

本文提出一种基于持久范畴建模的组合框架，通过SMT求解器同时合成硬件、软件和行为的设计与运行时组合，解决机器人系统在设计-部署范式下对变化环境适应性差的问题。

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.

Problem

• Lifelong Robot
• Compositional Framework
• Robustness
• Dynamic Reconfiguration
• Runtime Adaptation

Innovation

• compositional framework
• strict symmetric monoidal category
• SMT-based solver
• monoidal functors
• lifecycle-specific views