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RAMPA: robotic Augmented Reality for Machine programming by DemonstrAtion

IEEE robotICS AND AUTOMATION LETTERS 2025年第4期10卷 3795-3802页

作者： Dogangun, Fatih Bahar, Serdar Yildirim, Yigit Temir, Bora Toprak Ugur, Emre Dogan, Mustafa Doga Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkiye Adobe Res CH-4052 Basel Switzerland

This letter introduces robotic Augmented Reality for Machine programming by Demonstration (Rampa), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of programming by Demonstration (PbD) approaches on industrial robotic arms, e.g., Universal robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. Rampa addresses critical challenges of PbD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.

关键词： robots Trajectory Training Hands robot programming Data models Real-time systems Augmented reality User experience Human-robot interaction Virtual reality and interfaces learning from demonstration augmented reality (AR) extended reality (XR)

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Enhancing human-guided robotic assembly: AR-assisted DT for skill-based and low-code programming

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 74卷 676-689页

作者： Yin, Yue Zheng, Pai Li, Chengxi Wan, Ke Hong Kong Polytech Univ Res Inst Adv Mfg Dept Ind & Syst Engn Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Sci Pk Hong Kong Peoples R China

Efficient and natural programming strategies play a crucial role in enabling human -guided robotic assembly to adapt quickly to dynamic tasks. The combination of Augmented Reality (AR) and Digital Twins (DT) has shown promising potential in enhancing the intuitiveness of human-robot interaction while leveraging digital representations of human intelligence to empower robots in manufacturing tasks. However, traditional programming methods lack intuitive interaction and rely heavily on simulation environments or pre-set CAD models, leading to high costs for both initial setup and sim-to-real deployment. On the other hand, existing AR -based robot control methods have primarily focused on the basic movements of robots, overlooking higherlevel skills necessary for complex tasks. To address these limitations, this study introduces a four -layer system architecture that integrates AR -assisted DT into skill -based robotic assembly scenarios. Additionally, a skillbased and low -code programming system for human -guided robotic assembly is designed and implemented, which incorporates natural human guidance and robot autonomous intelligence to generate adaptive and feasible action plans. The feasibility and efficiency of the proposed system are verified by two case studies and a quantitative experiment comparing to traditional programming methods. The results demonstrate the usability of our AR -assisted DT approach in improving programming efficiency, intuitiveness, and safety for human -guided robotic assembly while reducing cognitive load.

关键词： Augmented reality Digital twin robot programming robotic assembly Human-robot interaction

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Probabilistic robotic logic programming with hybrid Boolean and Bayesian inference

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robotICA 2024年第1期42卷 40-71页

作者： Post, Mark A. Univ York Sch Phys Engn & Technol York YO10 5DD England

Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.

关键词： Bayesian methods robot programming logic programming probabilistic logic uncertain systems Boolean algebra

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A comprehensive review on configuration, design and programming of robotic systems used in various applications

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INTERNATIONAL JOURNAL OF INTELLIGENT robotICS AND APPLICATIONS 2025年 1-27页

作者： Kulkarni, Atharva Raut, Rituraj Dhatrak, Pankaj MIT World Peace Univ Dept Mech Engn Pune 411038 India

During the past decade, the domain of robotics has evolved immensely where they are being used in almost all aspects of daily life. They are being used in every sector like construction field, automotive industry, agriculture and many military applications. Jobs like maintaining electricity poles, cleaning sewer pipes, spray painting may have an ill effect on human life and hence, robots are used to do the work for humans. Repetitive tasks such as pick and place on an assembly line, drilling holes in a metal job, welding and painting are now done with the help of robots. The said process of deploying robots in place of human workers eliminates the margin of error that human workers can make, and it is highly time and cost efficient. The objective of the current review is to study robotic system design used over the years and the design procedures carried out by different authors. It covers numerous areas such as use of robots in factories/warehouses, construction sites, medical applications like lower limb rehabilitation and the use of robotic arm in CT scan, robot programming and other several applications.

关键词： Automation Gripper Design robotic Systems Design robot programming Medical Applications

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A Causal Bayesian Network and Probabilistic programming Based Reasoning Framework for robot Manipulation Under Uncertainty

arXiv

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arXiv 2024年

作者： Cannizzaro, Ricardo Groom, Michael Routley, Jonathan Ness, Robert Kunze, Lars Oxford Robotics Institute Dept. Engineering Science University of Oxford United Kingdom Microsoft Research RedmondWA United States Bristol Robotics Laboratory School of Engineering University of the West of England Bristol United Kingdom

robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes that are a barrier to their adoption. In this paper, we propose a flexible and generalisable physics-informed causal Bayesian network (CBN) based framework for a robot to probabilistically reason about candidate manipulation actions, to enable robot decision-making robust to arbitrary robot system uncertainties — the first of its kind to use a probabilistic programming language implementation. Using experiments in high-fidelity Gazebo simulation of an exemplar block stacking task, we demonstrate our framework’s ability to: (1) predict manipulation outcomes with high accuracy (Pred Acc: 88.6%);and, (2) perform greedy next-best action selection with 94.2% task success rate. We also demonstrate our framework’s suitability for real-world robot systems with a domestic robot. Thus, we show that by combining probabilistic causal modelling with physics simulations, we can make robot manipulation more robust to system uncertainties and hence more feasible for real-world applications. Further, our generalised reasoning framework can be used and extended for future robotics and causality research. Copyright © 2024, The Authors. All rights reserved.

关键词： robot programming

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A System for Human-robot Teaming through End-User programming and Shared Autonomy

arXiv

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arXiv 2024年

作者： Hagenow, Michael Senft, Emmanuel Radwin, Robert Gleicher, Michael Zinn, Michael Mutlu, Bilge Massachusetts Institute of Technology CambridgeMA United States Idiap Research Institute Martigny Switzerland University of Wisconsin – Madison MadisonWI United States

Many industrial tasks—such as sanding, installing fasteners, and wire harnessing—are difficult to automate due to task complexity and variability. We instead investigate deploying robots in an assistive role for these tasks, where the robot assumes the physical task burden and the skilled worker provides both the high-level task planning and low-level feedback necessary to effectively complete the task. In this article, we describe the development of a system for flexible human-robot teaming that combines state-of-the-art methods in end-user programming and shared autonomy and its implementation in sanding applications. We demonstrate the use of the system in two types of sanding tasks, situated in aircraft manufacturing, that highlight two potential workflows within the human-robot teaming setup. We conclude by discussing challenges and opportunities in human-robot teaming identified during the development, application, and demonstration of our system. © 2024, CC BY.

关键词： robot programming

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A zeroing feedback gradient-based neural dynamics model for solving dynamic quadratic programming problems with linear equation constraints in finite time

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Neural Computing and Applications 2024年第26期36卷 16395-16409页

作者： Du, Shangfeng Fu, Dongyang Jin, Long Si, Yang Li, Yongze School of Electronic and Information Engineering Guangdong Ocean University Zhanjiang524088 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China

Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroing feedback gradient-based neural dynamics (ZF-GND) models, is proposed based on the original GND model for tracking down the exact solution of dynamic quadratic programming problem (DQP). Further, a nonlinear projection function is designed to accelerate the convergence of the model. An upper bound on the convergence time of the ZF-GND model is rigorously defined through theoretical analysis. The superior effect of the ZF-GND model in terms of convergence is verified through comparison experiments. Finally, an application of robot motion planning is introduced to verify the practicality of the ZF-GND model. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： robot programming

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A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic programming With Application to robot Motion Planning

arXiv

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arXiv 2024年

作者： Yang, Yi Wang, Xuchen Voyles, Richard M. Ma, Xin The Multi-Scale Medical Robotics Center The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong The School of Engineering Technology Purdue University West LafayetteIN47907 United States

This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain ZNN spectrum, known as FOZNNs, features diminishing gains over time and marries noise resistance with predefined-time convergence, making it ideal for energy-efficient robotic motion planning tasks. The PTC-NT-FOZNN enhances traditional ZNN models by incorporating a newly developed activation function that promotes optimal convergence irrespective of the model’s order. When evaluated against six established ZNNs, the PTC-NT-FOZNN, with parameters 0 © 2024, CC BY.

关键词： robot programming

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programming of a Lightweight robot Using Function Blocks and Sequential Function Charts 25

Programming of a Lightweight Robot Using Function Blocks and...

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25th International Conference on Methods and Models in Automation and robotics

作者： Thormann, Christian Winkler, Alexander Univ Appl Sci Hsch Mittweida Dept Automat Mittweida Germany

ISBN: (纸本)9781728173818;9781728173801

This paper presents an approach to realize a universal robot programming language. Currently, numerous programming languages for industrial robots exit, depending on the manufacturer of the robot. Attempts to create a standardized language have failed in the past. The presence of several new providers of robots on the market increases the number of different programming languages and concepts. For this reason, it would be desirable to have a unified procedure as can be found in the field of programmable logic controllers. Here, a specification is valid, which defines certain types of programming languages. In this contribution, a lightweight robot was connected to a programmable logic controller, where the programming of the robot should be performed. For this purpose, the communication between these two partner devices was achieved using precast data structures. Concerning the programmable logic controller, function blocks were developed to operate the robot and its periphery. They include robot-based functionality, known from common robot programming languages. With regard to the robot, a client program, which interprets and executes the commands from the programmable logic controller, was implemented. Some small robot tasks were performed to show the simplicity of our approach to robot programming based on the languages of programmable logic controllers.

关键词： robot programming function block sequential function chart intuitive programming

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A warm hug from a robot: A dual-mode e-skin with programming compliance

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REVIEW OF SCIENTIFIC INSTRUMENTS 2022年第11期93卷 115007-115007页

作者： Liu, Zhe Chen, Diansheng Ma, Junlin Shen, Zhenyang Wu, Tianhao Jia, Zining Jiang, Yongkang Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

Recent achievements in the field of electronic skin (e-skin) have provided promising technology for service robots. However, the development of a bionic perception system that exhibits superior performance in terms of safety and interaction quality remains a challenge. Here, we demonstrate a biomimetic soft e-skin that is composed of an array of capacitors and air pouches. It is a single platform that shows dual-mode sensing capabilities of tactile sensing and proximity perception. We optimized the shape and area of the electrode via simulation of the approach of a robot to an object. Moreover, the compliance and temperature of the e-skin can be actively adjusted by tuning the pressure and heat of the air inside the pouches. The e-skin provided dual-mode sensing feedback and soft touch for humanoid service robots, for example, when a robot hugged a man, which illustrated the potential of this e-skin for applications in human-robot interactions. Published under an exclusive license by AIP Publishing.

关键词： robot programming

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