发表机构
AI Institute, School of Computer Science, Shanghai Jiao Tong University; Differential Robotics Technology Co., Ltd.（上海交通大学计算机科学与工程系人工智能研究院; 差分机器人技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对空中抓取轨迹优化非凸、初始化敏感且目标函数难以完全反映任务成功的问题，本文提出学习轨迹先验并借助CEM演化，结合执行感知评论家，提升优化可靠性与抓取性能。
AI 中文摘要
空中抓取是捕食性鸟类表现出的一种非凡能力，使它们能够通过飞行中高度协调的机动来捕获猎物。受此能力启发，研究人员开发了各种公式，通过轨迹优化来复现此类机动。然而，在实践中仍存在两个局限。首先，由此产生的优化问题高度非凸且对初始化敏感，使得在有限的计算预算下难以获得高质量解。其次，规定的数值目标是人为设计的抽象，通过一组有限的数学上可处理的量来描述成功抓取，可能无法完全捕捉决定任务成功的因素。我们研究学习如何在分析型规划器中解决这些局限。据此，首先从优化运动中学习一个轨迹先验，然后通过基于CEM的过程进行演化，该过程使用部署的优化器评估采样的初始化，并保留有利的初始化作为新的监督。一个执行感知评论家从接触、提升和完成结果中学习，以评估优化后的轨迹在物理执行中成功的可能性。其冻结的能量可进一步用作可微的抓取成本，使执行数据直接塑造轨迹生成。仿真和真实世界实验证明了优化可靠性、轨迹一致性和抓取性能的提升。
英文摘要
Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain in practice. First, the resulting optimization problem is highly nonconvex and sensitive to initialization, making high-quality solutions difficult to obtain under a limited computational budget. Second, prescribed numerical objectives are human-designed abstractions that describe successful grasping through a limited set of mathematically tractable quantities and may not fully capture what determines task success. We investigate how learning can address these limitations within an analytical planner. Accordingly, a trajectory prior is first learned from optimized motions and then evolved through a CEM-based process that evaluates sampled initializations with the deployed optimizer and retains favorable ones as new supervision. An Execution-Aware Critic learns from contact, lift, and completion outcomes to assess whether the optimized trajectories are likely to succeed in physical execution. Its frozen energy can further serve as a differentiable grasping cost, allowing execution data to directly shape trajectory generation. Simulation and real-world experiments demonstrate improved optimization reliability, trajectory consistency, and grasping performance.
Comments20 pages, 14 figures
