通过轨迹优化与符号修复实现地形自适应运动的物理可行反应式综合方法
Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion via Trajectory Optimization and Symbolic Repair
AI总结：
该研究提出一种集成反应式综合、混合整数凸规划与符号修复的四足机器人运动规划框架，可在动态不可预见地形上实现物理可行的自适应运动，提升了综合可扩展性与在线执行适配性。
摘要：
我们提出了一种面向四足机器人在动态变化、不可预见地形上运动的集成规划框架。现有方法要么依赖启发式方法进行瞬时落脚点选择——这会牺牲安全性与通用性——要么需要求解包含复杂地形特征和长时间跨度的高成本轨迹优化问题。与之不同，我们的框架在符号层面利用反应式综合生成构造即正确的控制器，并采用混合整数凸规划（MICP）为每个符号转换生成具备动态可行性与物理可行性的步点规划。我们通过高层管理器引入局部环境信息，缩减综合过程中的大状态空间，提升了综合的可扩展性。为处理因动态不可行而无法满足的规格，并减少高成本的MICP求解次数，我们利用符号修复过程仅生成必要的符号转换。在线执行阶段，结合真实地形数据重新运行MICP，搭配运行时符号修复，填补了离线综合与在线执行之间的差距。我们在仿真中验证了该框架的能力：它能够发现缺失的运动技能，并在散落的踏脚石、钢筋等安全关键环境中做出及时反应。
英文摘要：
We propose an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing approaches either rely on heuristics for instantaneous foothold selection--compromising safety and versatility--or solve expensive trajectory optimization problems with complex terrain features and long time horizons. In contrast, our framework leverages reactive synthesis to generate correct-by-construction controllers at the symbolic level, and mixed-integer convex programming (MICP) for dynamic and physically feasible footstep planning for each symbolic transition. We use a high-level manager to reduce the large state space in synthesis by incorporating local environment information, improving synthesis scalability. To handle specifications that cannot be met due to dynamic infeasibility, and to minimize costly MICP solves, we leverage a symbolic repair process to generate only necessary symbolic transitions. During online execution, re-running the MICP with real-world terrain data, along with runtime symbolic repair, bridges the gap between offline synthesis and online execution. We demonstrate, in simulation, our framework's capabilities to discover missing locomotion skills and react promptly in safety-critical environments, such as scattered stepping stones and rebars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
