从视觉演示中学习关键点约束以实现安全运动规划
Learning Keypoint Constraints from Visual Demonstrations for Safe Motion Planning
AI总结：
该研究提出KCL框架，通过视觉演示学习任务相关运动约束，结合关键点感知与动力学误差分析，在导航、操作等任务的安全规划中表现优于基线方法。
摘要：
我们提出了关键点约束学习（KCL）框架，该框架用于从视觉演示中学习与任务相关的运动约束，并利用提取的约束进行安全规划。为了使从RGB-D观测中学习约束变得可行，我们通过分割、视觉特征聚类和点跟踪提取稀疏的3D物体中心关键点和语义掩码质心来表示演示。在这个简化的几何空间中，我们学习关键点动力学并推断基于距离和方向的约束，通过在简化状态空间中执行KKT最优性条件，使专家轨迹可行且近似局部最优。随后，我们利用从累积预测误差导出的鲁棒裕度，在不确定性下使用学习到的约束进行规划，并对关键点感知误差和学习到的动力学误差如何影响恢复的约束进行理论分析。我们在从导航到刚性/可变形操作的视觉演示上评估KCL，结果表明它能恢复多阶段几何约束，且在下游安全规划中优于基线方法。
英文摘要：
We present Keypoint Constraint Learning (KCL), a framework for learning task-relevant motion constraints from visual demonstrations and for planning safely with the extracted constraints. To make constraint learning tractable from RGB-D observations, we represent demonstrations using sparse 3D object-centric keypoints and semantic mask centroids extracted through segmentation, visual feature clustering, and point tracking. In this reduced geometric space, we learn keypoint dynamics and infer distance- and orientation-based constraints that make the expert trajectories feasible and approximately locally optimal by enforcing KKT optimality conditions in the reduced state space. We then plan with the learned constraints under uncertainty, using robustness margins derived from accumulated prediction error, and provide a theoretical analysis of how keypoint perception error and learned dynamics error affect the recovered constraints. We evaluate KCL on visual demonstrations from navigation to rigid/deformable manipulation, showing that it recovers multi-stage geometric constraints and outperforms baselines in downstream safe planning.
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。
