AdapJ: An Adaptive Extended Jacobian Controller for Soft Manipulators
Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（生物机器人研究所和机器人与人工智能卓越部门，圣安娜高等学院）；Multi-scale Medical Robotics Centre and Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong（多尺度医疗机器人中心和创新医学技术中心，香港中文大学）；Department of computer systems, Tallinn University of Technology（计算机系统系，塔林技术大学）
The nonlinearity and hysteresis of soft robot motions present challenges for control. To solve these issues, the Jacobian controller has been applied to approximate the nonlinear behaviors in a linear format. Accurate controllers like neural networks can handle delayed and nonlinear motions, but they require large datasets and exhibit low adaptability. Based on a novel analysis on these controllers, we propose an adaptive extended Jacobian controller, AdapJ, for soft manipulators. This controller retains the concise format of the Jacobian controller but introduces independent parameters. Similar to neural networks, its initialization and updating mechanism leverages the inverse model without building the corresponding forward model. In the experiments, we first compare the performance of the Jacobian controller, model predictive controller, neural network controller, iterative feedback controller, and AdapJ in simulation. We further analyze how AdapJ parameters adapt in response to the physical property change. Then, real-world experiments have validated that AdapJ outperforms the neural network controller, model predictive controller, and iterative feedback controller with fewer training samples and adapts robustly to varying conditions, including different control frequencies, material softness, and external disturbances. Future work may include online adjustment of the controller format and adaptability validation in more scenarios.
Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation
Kent Artificial Intelligence Laboratory（肯特人工智能实验室）
除了解决需要一定智能水平的复杂问题外，微调的深度神经网络还可以为缓慢的数值任务创建快速算法。在本文中，我们介绍了[1]工作的改进版本，这是一个快速的深度学习框架，能够生成串联连杆机械手的完整工作空间。该体系结构由两个神经网络：一个估计网，近似的机械手雅可比矩阵，和一个置信度网，测量的近似的信心。我们还介绍了M3（Manipulability Maps of Manipulators），一个基于[2]（RTB）的MATLAB机器人库，它生成的数据集被这项工作所使用。结果表明，神经网络不仅比数值逆运动学快得多，而且与其他机器学习替代方案相比，它还提供了更高的精度。该算法的实现（基于Keras[3]），包括基准评估脚本，可在https://github.com/liaopeiyuan/Jacobian-Estimation上获得。M3库API和数据集也可在https://github.com/liaopeiyuan/M3上获得。
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
1 Department of Electronics Systems, Aalborg University, Denmark；3 Extreme Robotics Laboratory, School of Metallurgy \& Materials, University of Birmingham, Birmingham, United Kingdom
Motion planning for highly-dynamic unconditioned reflexes based on chained Signed Distance Functions
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Robotics and Artificial Intelligence Laboratory, The Chinese University of Hongkong, Shenzhen（香港中文大学（深圳）机器人与人工智能实验室）；University College London（伦敦大学学院）
Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking
FZI Research Center for Information Technology（FZI信息技术研究中心）；IAR Institute for Anthropomatics and Robotics（IAR拟人系统与机器人研究所）；KIT Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
