Control of Tendon-Driven Soft Foam Robot Hands
Control of Tendon-Driven Soft Foam Robot Hands
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DOI：
10.1109/humanoids.2018.8624937
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
C. Schlagenhauf;Dominik Bauer;Kai-Hung Chang;J. King;Daniele Moro;Stelian Coros;N. Pollard
中科院分区：
文献类型：
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作者：
C. Schlagenhauf;Dominik Bauer;Kai-Hung Chang;J. King;Daniele Moro;Stelian Coros;N. Pollard
针对柔性机械臂提出了一系列控制策略。我们提供了一种新的方法来控制多指肌腱驱动的泡沫手，使用CyberGlove和一个简单的岭回归模型。所取得的结果包括复杂的姿势，灵活的抓取和手部操作。为了实现高效的数据采集和更直观的泡沫机器人设计过程，我们实现并评估了基于有限元的模拟。使用VICON运动捕捉系统对该模型的准确性进行了评估。然后利用该仿真方法求解运动学逆解，并比较了监督学习、强化学习、最近邻和线性岭回归方法在精度和样本效率方面的性能。
This paper presents a series of control strategies for soft compliant manipulators. We provide a novel approach to control multi-fingered tendon-driven foam hands using a CyberGlove and a simple ridge regression model. The results achieved include complex posing, dexterous grasping and in-hand manipulations. To enable efficient data sampling and a more intuitive design process of foam robots, we implement and evaluate a finite element based simulation. The accuracy of this model is evaluated using a Vicon motion capture system. We then use this simulation to solve inverse kinematics and compare the performance of supervised learning, reinforcement learning, nearest neighbor and linear ridge regression methods in terms of their accuracy and sample efficiency.