论文标题
Sydebo：在动态环境中为长马操作的符号二十分束缚的双杆优化
SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments
论文作者
论文摘要
这项研究提出了一种任务和运动计划（TAMP）方法，该方法具有嵌入在双重优化中的符号决策。这种TAMP方法利用了在动态变化的环境中为长马和多功能任务的顺序操作的离散结构。在符号计划级别，我们建议使用因果图分解的计划域定义语言（PDDL），针对长马操纵任务进行可扩展的决策方法。在运动计划级别，我们根据乘数的交替方向方法（ADMM）设计了一种轨迹优化（TO）方法，该方法适用于以分布式方式求解受约束的大规模非线性优化。与传统的几何运动计划者不同，我们的方法通过结合完整的机器人和对象动态来产生高度动态的操作动作。此外，为代替层次计划方法，我们解决了一个整体整合的双层优化问题，该问题涉及从低级到和高级搜索的成本。模拟和实验结果表明，在混乱和移动输送带中，长马对象分类任务的动态操作。
This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamically changing environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Alternating Direction Method of Multipliers (ADMM), suitable for solving constrained, large-scale nonlinear optimization in a distributed manner. Distinct from conventional geometric motion planners, our approach generates highly dynamic manipulation motions by incorporating the full robot and object dynamics. Furthermore, in lieu of a hierarchical planning approach, we solve a holistically integrated bilevel optimization problem involving costs from both the low-level TO and the high-level search. Simulation and experimental results demonstrate dynamic manipulation for long-horizon object sorting tasks in clutter and on a moving conveyor belt.
