Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning
Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning
复制标题
DOI：
10.1109/lra.2020.2976320
复制
发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Rus, Daniela
中科院分区：
文献类型：
--
作者：
Truby, Ryan L.;Della Santina, Cosimo;Rus, Daniela
创建具有复杂自主功能的软机器人需要这些系统通过集成软传感器拥有可靠的在线 3D 配置本体感知。我们提出了一个框架，用于利用柔软的本体感受传感器皮肤的反馈，通过深度学习来预测软机器人的 3D 配置。我们的框架引入了一种支持剪纸的策略，用于使用现成的材料快速感知软机器人、软机器人几何形状的一般运动学描述，以及用于预测软机器人配置的神经网络设计的研究。即使来自压阻传感器的迟滞、非单调反馈，循环神经网络也显示出预测新运动学参数以及机器人配置的潜力。尽管未完全捕获完整的动态行为，但经过训练的神经网络可以密切预测操作期间的稳态配置。我们在带有 12 个离散执行器和 12 个本体感觉传感器的象鼻臂上验证了我们的方法。作为软机器人感知的重要进步，我们预计我们的框架将为软机器人闭环控制开辟新途径。
Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot's 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot's configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics.