Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction
Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction
复制标题
具有学习零步和一步捕获预测的稳健人形接触规划
DOI：
--
复制
发表时间：
2019
影响因子：
5.2
通讯作者：
D. Berenson
中科院分区：
文献类型：
--
作者：
Yu;L. Righetti;D. Berenson
人形机器人通过控制施加在环境中的接触扳手来保持平衡和导航。虽然可以使用现有方法来规划应用适当扳手的动态可行运动，但人形机器人也可能受到外部干扰的影响。现有的系统通常依赖于控制器从干扰中反应性地恢复。然而，当机器人不能到达能够拒绝给定干扰的触点时，这样的控制器可能失效。在这封信中，我们提出了一个基于搜索的脚步规划器，其目的是最大限度地提高机器人成功达到目标的概率，而不会因为干扰而下降。规划器不仅考虑了规划接触序列的姿态，而且还考虑了规划接触序列附近的可用于从外部干扰中恢复的替代接触。虽然这种额外的考虑显着增加了计算负荷，我们训练神经网络，以有效地预测多接触零步和一步的可捕获性，这使得规划器有效地生成强大的接触序列。我们的研究结果表明，我们的方法产生的足迹序列，更强大的外部干扰比传统的足迹规划在四个具有挑战性的情况下。
Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this letter, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.
使用学习质心动力学预测进行高效的人形接触规划
DOI：
10.1109/icra.2019.8794032
发表时间：
2019
期刊：
2019 IEEE International Conference on Robotics and Automation (ICRA
影响因子：
--
作者：
Lin, Yu-Chi;Ponton, Brahayam;Righetti, Ludovic;Berenson, Dmitry
通讯作者：
Berenson, Dmitry