In this paper, we explore the optimization of hyperparameters for the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the Tree-structured Parzen Estimator (TPE) in the context of robotic arm control with seven Degrees of Freedom (DOF). Our results demonstrate a significant enhancement in algorithm performance, TPE improves the success rate of SAC by 10.48 percentage points and PPO by 34.28 percentage points, where models trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward within 95% of the maximum reward 76% faster than without TPE, which translates to about 40K fewer episodes of training required for optimal performance. Also, this improvement for SAC is 80% faster than without TPE. This study underscores the impact of advanced hyperparameter optimization on the efficiency and success of deep reinforcement learning algorithms in complex robotic tasks.
Princeton University（普林斯顿大学）；Nokia Bell Labs（诺基亚贝尔实验室）
Re-configurable robots have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. Here, we focus on robotic arms with multiple rigid links connected by joints. We propose a deep reinforcement learning agent with sequence neural networks embedded in the agent to adapt to robotic arms that have a varying number of links. Further, with the additional tool of domain randomization, this agent adapts to different configurations. We perform simulations on a 2D N-link arm to show the ability of our netwo…
Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuou…
Generating and Customizing Robotic Arm Trajectories using Neural Networks
Faculty of Mathematics, Physics and Informatics（数学、物理与信息学系）；Comenius University Bratislava（布拉迪斯拉发康门尼大学）；DIBRIS（迪布里斯）；University of Genoa（热那亚大学）；Italian Institute of Technology（意大利理工学院）
Hanson Robotics Ltd（汉森机器人有限公司）；Indian Institute of Science（印度科学学院）；ARTPARK（ARTPARK（人工智能与机器人技术园区））
Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have desig…
A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of F…
Inverse Kinematics (IK) solves the problem of mapping from the Cartesian space to the joint configuration space of a robotic arm. It has a wide range of applications in areas such as computer graphics, protein structure prediction, and robotics. With the vast advances of artificial neural networks (NNs), many researchers recently turned to data-driven approaches to solving the IK problem. Unfortunately, NNs become inadequate for robotic arms with redundant Degrees-of-Freedom (DoFs). This is because such arms may have multiple angle solutions to reach the same desired pose, while typical NNs on…
D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院人工智能与机器人中心）；School of Advanced Manufacturing, Sun Yat-sen University（中山大学先进制造学院）
Chittagong University of Engineering & Technology（吉大港工程与技术大学）
This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is controlled by human voice and has been developed in the Mechatronics Laboratory, CUET. This robotic arm is interfaced with a PC by serial communication (RS-232). Users' voice command is captured by a microphone, and this voice is processed by software which is made by Microsoft visual studio. Then the specific signal (obtained by signal processing) is sent to control unit. The main control unit that is used in the robotic arm is a microcontroller whose model no. is PIC18f452. Then Control unit drives the actuators, (Hi…
Design And Control of A Robotic Arm For Industrial Applications
CMR Technical Campus（CMR技术学院）；JNTUH University College of Engineering, Science & Technology Hyderabad（贾瓦哈拉尔·尼赫鲁科技大学海得拉巴工程科学与技术学院）；Holy Mary Institute of Technology（圣玛丽技术学院）
Robotic deformable-object manipulation is a challenge in the robotic industry because deformable objects have complicated and various object states. Predicting those object states and updating manipulation planning is time-consuming and computationally expensive. In this paper, we propose learning known configurations of garments to allow a robot to recognise garment states and choose a pre-designed manipulation plan for garment flattening.
Accenture Labs（埃森哲实验室）；National Research Council Canada（加拿大国家研究委员会）
In this paper, we explore the ability of a robot arm to learn the underlying operation space defined by the positions (x, y, z) that the arm's end-effector can reach, including disturbances, by deploying and thoroughly evaluating a Spiking Neural Network SNN-based adaptive control algorithm. While traditional control algorithms for robotics have limitations in both adapting to new and dynamic environments, we show that the robot arm can learn the operational space and complete tasks faster over time. We also demonstrate that the adaptive robot control algorithm based on SNNs enables a fast res…
Westphalian University of Applied Sciences（威斯特伐利亚应用科学大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
Living a self-determined life independent of human caregivers or fully autonomous robots is a crucial factor for human dignity and the preservation of self-worth for people with motor impairments. Assistive robotic solutions - particularly robotic arms - are frequently deployed in domestic care, empowering people with motor impairments in performing ADLs independently. However, while assistive robotic arms can help them perform ADLs, currently available controls are highly complex and time-consuming due to the need to control multiple DoFs at once and necessary mode-switches. This work provide…
Controlling soft continuum robotic arms is challenging due to their hyper-redundancy and dexterity. In this paper we demonstrate, for the first time, closed-loop control of the configuration space variables of a soft robotic arm, composed of independently controllable segments, using a Cosserat rod model of the robot and the distributed sensing and actuation capabilities of the segments. Our controller solves the inverse dynamic problem by simulating the Cosserat rod model in MATLAB using a computationally efficient numerical solution scheme, and it applies the computed control output to the a…
Robotic-assisted Ultrasound for Fetal Imaging: Evolution from Single-arm to Dual-arm System
King’s College London（伦敦国王学院）；Xtronics Ltd.（Xtronics有限公司）；Guy’s & St Thomas’ NHS Foundation Trust（盖伊和圣托马斯NHS基金会信托）；Evelina Children’s Hospital（伊芙琳娜儿童医院）；Queen Mary University of London（伦敦玛丽女王大学）
Design and Control of a Novel Six-Degree-of-Freedom Hybrid Robotic Arm
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences（北京农林科学院智能装备研究中心）；School of Mechanical Electrical Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）
Shanghai Jiao Tong University（上海交通大学）；University of Oxford（牛津大学）；Tencent（腾讯）
Collision avoidance is a widely investigated topic in robotic applications. When applying collision avoidance techniques to a mobile robot, how to deal with the spatial structure of the robot still remains a challenge. In this paper, we design a configuration-aware safe control law by solving a Quadratic Programming (QP) with designed Control Barrier Functions (CBFs) constraints, which can safely navigate a mobile robotic arm to a desired region while avoiding collision with environmental obstacles. The advantage of our approach is that it correctly and in an elegant way incorporates the spati…
Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the robotic arm. In this study, we propose a knowledge distillation-based framework to manipulate robotic arm through hybrid paradigm induced EEG signals for practical use. The teacher model is designed to decode input data hierarchically and transfer knowledge to student model. To this end, soft labels and distillation loss functions are applied to the student model training. According to experimental results, student model a…
Continuous Collision Detection for a Robotic Arm Mounted on a Cable-Driven Parallel Robot
Tecnalia France（法国泰克纳利亚）；LAAS-CNRS（法国国家科学研究中心自动化系统与机器人实验室）；University of Toulouse（图卢兹大学）；LIRMM（蒙彼利埃大学与法国国家科学研究中心联合机器人与微电子机械系统实验室）；Université de Montpellier（蒙彼利埃大学）；CNRS（法国国家科学研究中心）
