Learning Stable Dynamics via Iterative Quadratic Programming
Learning Stable Dynamics via Iterative Quadratic Programming
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DOI：
10.1109/icra48891.2023.10161237
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Paul Gesel;M. Begum
中科院分区：
文献类型：
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作者：
Paul Gesel;M. Begum
提出了一种新型的基于自主动态系统（ADS）的轨迹演示学习（LfD）控制器。我们将这种方法称为通过迭代二次规划学习稳定动力学（LSD-IQP）。LSD-IQP通过半无限二次规划的演示学习能量函数和ADS。对学习后的ADS施加能量函数约束，保证其收敛到单个目标位置。与其他基于能量的方法不同，LSD-IQP允许能量函数同时具有局部最大值和鞍点。与其他基于ads的控制器相比，这种灵活性使LSD-IQP能够学习更广泛的运动类别。我们通过几个实验证明了LSD-IQP的能力，包括：1)学习手写符号并将扫描误差区域与其他几种ADS方法进行比较；2)学习具有新目标位置的机器人拾取任务；3)学习机器人在非凸障碍物存在下的点对点运动。
This paper proposes a novel autonomous dynamic system (ADS) based controller for trajectory learning from demonstration (LfD). We call our method Learning Stable Dynamics via Iterative Quadratic Programming (LSD-IQP). LSD-IQP learns an energy function and an ADS from demonstrations via semi-infinite quadratic programming. Energy function constraints are imposed on the learned ADS to ensure convergence to a single goal position. Unlike other energy-based methods, LSD-IQP allows the energy function to have both local maximums and saddle points. This flexibility enables LSD-IQP to learn a broader class of motions compared to other ADS-based controllers. We demonstrate the capabilities of LSD-IQP via several experiments, including: 1) learning handwritten symbols and comparing the swept error area to several other ADS methods 2) learning a pick-and-place task with novel goal positions for a robot, and 3) learning a point to point motion in the presence of a non-convex obstacle for a robot.