Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion
Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion
复制标题
学习如何行走：具有运动记忆的热启动最优控制求解器
DOI：
10.1109/icra40945.2020.9196727
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Calinon
中科院分区：
文献类型：
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作者：
Teguh Santoso Lembono;Carlos Mastalli;Pierre Fernbach;N. Mansard;S. Calinon
在本文中，我们提出了一个框架来建立运动记忆的暖启动和最优控制求解器的人形机器人的运动任务。我们使用多功能运动规划器HPP Loco3D离线生成一组动态一致的全身轨迹，并将其存储为运动记忆。学习问题被表述为一个回归问题，在给定期望的接触位置的情况下预测单步运动，这是产生多步运动的基础。然后将预测的运动用作快速最优控制解算器crocodildyl的热启动。我们已经证明，该方法设法减少了所需的迭代次数，以达到收敛，从单步运动的~ 9.5迭代到仅~ 3.0迭代，从多步运动的~ 6.2迭代到~ 4.5迭代，同时保持解决方案的质量。
In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consistent whole-body trajectory to be stored as the memory of motion. The learning problem is formulated as a regression problem to predict a single-step motion given the desired contact locations, which is used as a building block for producing multi-step motions. The predicted motion is then used as a warm-start for the fast optimal control solver Crocoddyl. We have shown that the approach manages to reduce the required number of iterations to reach the convergence from ∼9.5 to only ∼3.0 iterations for the single-step motion and from ∼6.2 to ∼4.5 iterations for the multi-step motion, while maintaining the solution’s quality.