用于地形自适应运动的物理可行反应式合成
Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion
AI总结：
针对四足动物在动态变化地形的运动规划问题，提出综合规划框架，结合反应式合成与混合整数凸规划，采用符号修复机制，经实验验证该框架能识别缺失技能并在关键环境有效响应。
摘要：
我们提出了一个用于四足动物在动态变化、不可预见地形上运动的综合规划框架。现有方法在实时立足点选择上常依赖启发式方法，限制了鲁棒性和适应性，或依赖复杂地形和长视野上计算密集的轨迹优化。相比之下，我们的方法将用于生成构造正确的符号级控制器的反应式合成与用于每个符号转换期间动态且物理可行的脚步规划的混合整数凸规划相结合。为减少对昂贵的混合整数凸规划求解的依赖并适应因物理不可行可能违反的规范，我们采用一种符号修复机制，仅选择性地生成所需的符号转换。在执行过程中，基于实际地形数据的实时混合整数凸规划重新规划，结合运行时符号修复和延迟感知协调，实现离线合成与在线操作之间的无缝衔接。通过广泛的模拟和硬件实验，我们验证了该框架识别缺失运动技能并在安全关键环境中有效响应的能力，包括散落的踏脚石和钢筋场景。
英文摘要：
We present an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing methods often depend on heuristics for real-time foothold selection-limiting robustness and adaptability-or rely on computationally intensive trajectory optimization across complex terrains and long horizons. In contrast, our approach combines reactive synthesis for generating correct-by-construction symbolic-level controllers with mixed-integer convex programming (MICP) for dynamic and physically feasible footstep planning during each symbolic transition. To reduce the reliance on costly MICP solves and accommodate specifications that may be violated due to physical infeasibility, we adopt a symbolic repair mechanism that selectively generates only the required symbolic transitions. During execution, real-time MICP replanning based on actual terrain data, combined with runtime symbolic repair and delay-aware coordination, enables seamless bridging between offline synthesis and online operation. Through extensive simulation and hardware experiments, we validate the framework's ability to identify missing locomotion skills and respond effectively in safety-critical environments, including scattered stepping stones and rebar scenarios.
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。
