Predictive Learning of Error Recovery with a Sensorized Passivity-Based Soft Anthropomorphic Hand
Predictive Learning of Error Recovery with a Sensorized Passivity-Based Soft Anthropomorphic Hand
复制标题
使用基于传感无源性的软拟人手进行错误恢复的预测学习
DOI：
10.1002/aisy.202200390
复制
发表时间：
2023
影响因子：
7.4
通讯作者：
Gilday K
中科院分区：
文献类型：
--
作者：
Gilday K
基于软体相互作用的被动动力学的操纵策略提供了具有有限感觉信息的稳健性能。它们利用身体的运动学结构和被动动力学来适应不同形状和属性的物体。然而，这些软被动交互使得机器人设备的状态受到环境的影响，使得控制生成和状态估计困难。这项工作提出了一个基于动态交互的抓取的闭环框架，它依赖于两个新颖性：1）手腕驱动的被动柔软拟人手，可以使用一步动觉教学生成强大的抓取策略; 2）基于学习的感知系统，使用来自稀疏触觉传感器的时间数据来预测和适应故障。通过拟人软件设计和手腕驱动控制，可以生成对新对象和位置不确定性具有鲁棒性的控制器。通过基于学习的高级感知系统和32个感知受体，可以提前预测故障，进一步提高整个系统的鲁棒性，使抓取成功率提高一倍以上。从超过1000个真实的抓握试验中，控制和感知框架也被认为可以转移到新的物体和条件。这篇文章的互动预印本可以在这里找到：https://doi.org/10.22541/au.167687985.55182112/v1。
Manipulation strategies based on the passive dynamics of soft‐bodied interactions provide robust performances with limited sensory information. They utilize the kinematic structure and passive dynamics of the body to adapt to objects of varying shapes and properties. However, these soft passive interactions make the state of the robotic device influenced by the environment, making control generation and state estimation difficult. This work presents a closed‐loop framework for dynamic interaction‐based grasping that relies on two novelties: 1) a wrist‐driven passive soft anthropomorphic hand that can generate robust grasp strategies using one‐step kinaesthetic teaching and 2) a learning‐based perception system that uses temporal data from sparse tactile sensors to predict and adapt to failures before it happens. With the anthropomorphic soft design and wrist‐driven control, it is shown that controllers can be generated robust to novel objects and location uncertainty. With the learning‐based high‐level perception system and 32 sensing receptors, it is shown that failures can be predicted in advance, further improving the robustness of the entire system by more than doubling the grasping success rate. From over 1000 real‐world grasping trials, both the control and perception framework are also seen to be transferable to novel objects and conditions. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.167687985.55182112/v1.