Dynamic Movement Primitives Based Robot Skills Learning

In this article,a robot skills learning framework is developed,which considers both motion modeling and execution.In order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators.The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences.In addition,motions are categorized into different goals and durations.It is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning system.The experiment test on the Baxter robot verifies the effectiveness of the proposed method.

Shape estimation for a TPU-based multi-material 3D printed soft pneumatic actuator using deep learning models

Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception parameters.To tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)filaments.We designed various geometric shapes for the sensors and investigated their strain-resistive performance during deformation.To address the nonlinear time-variant behavior of the sensors during dynamic modeling,we adopted a data-driven approach using different deep neural networks to learn the relationship between sensor signals and system states.A series of experiments in various actuation scenarios were conducted,and the results demonstrated the effectiveness of this approach.The sensing and shape prediction steps can run in real-time at a frequency of50 Hz on a consumer-level computer.Additionally,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor failures.All the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape estimation.The aim of this study is to introduce a methodology for the proprioception of soft pneumatic actuators,including manufacturing and sensing modeling,that can be generalized to other soft robots.

From Teleoperation to Autonomous Robot-assisted Microsurgery:A Survey

Robot-assisted microsurgery(RAMS)has many benefits compared to traditional microsurgery.Microsurgical platforms with advanced control strategies,high-quality micro-imaging modalities and micro-sensing systems are worth developing to further enhance the clinical outcomes of RAMS.Within only a few decades,microsurgical robotics has evolved into a rapidly developing research field with increasing attention all over the world.Despite the appreciated benefits,significant challenges remain to be solved.In this review paper,the emerging concepts and achievements of RAMS will be presented.We introduce the development tendency of RAMS from teleoperation to autonomous systems.We highlight the upcoming new research opportunities that require joint efforts from both clinicians and engineers to pursue further outcomes for RAMS in years to come.

Efficient learning of robust quadruped bounding usingpretrained neural networks

Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles.The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements.The authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding performance.The NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’approach.The authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.The cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://doi.org/10.1049/csy2.12062.