Learning to Control Reconfigurable Staged Soft Arms
Learning to Control Reconfigurable Staged Soft Arms
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DOI：
10.1109/icra40945.2020.9197516
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
A. Nicolai;G. Olson;Y. Mengüç;Geoffrey A. Hollinger
中科院分区：
文献类型：
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作者：
A. Nicolai;G. Olson;Y. Mengüç;Geoffrey A. Hollinger
在这项工作中，我们提出了一种新的方法建模，并分类之间，系统的负载状态时，建立分阶段的软臂配置。通过两个阶段的方法：（1）使用LSTM校准例程来识别当前负载状态，然后（2）控制输入生成步骤将广义准静态模型与学习的负载模型组合。我们的实验表明，占系统负载允许我们更准确地控制锥形臂配置。我们分析了我们的方法使用软机器人执行器的性能，并表明它能够以大于95%的速度在不同的手臂配置之间进行分类。此外，我们的方法是能够减少末端执行器误差的准静态模型只控制我们的控制器基线的1厘米内。
In this work, we present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Through a two stage approach: (1) an LSTM calibration routine is used to identify the current load state then (2) a control input generation step combines a generalized quasistatic model with the learned load model. Our experiments show that accounting for system load allows us to more accurately control tapered arm configurations. We analyze the performance of our method using soft robotic actuators and show it is capable of classifying between different arm configurations at a rate greater than 95%. Additionally, our method is capable of reducing the end-effector error of quasistatic model only control to within 1 cm of our controller baseline.