论文标题
预测对人类机器人共生行走的周期性行为的预测建模
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
论文作者
论文摘要
我们在本文中提出了周期性互动原始词 - 一种概率框架，可用于学习紧凑的周期性行为模型。我们的方法将现有的互动原始形式扩展到定期运动制度，即步行。我们表明，该模型特别适合学习人类步行的数据驱动，定制的模型，然后可以用于生成对未来状态的预测或推断潜在的生物力学变量。我们还展示了如何使用模仿学习方法来使用相同的框架来学习机器人假体的控制器。进行人类参与者实验的结果表明，定期相互作用原语有效地产生了预测和脚踝控制信号，用于机器人肢体踝关节，MAE为2.21度，每次推理0.0008s。在存在噪音或传感器下降的情况下，性能优雅地退化。与替代方案相比，该算法的功能快20倍，并且在测试对象上的执行速度更高4.5倍。
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21 degrees in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
