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Information-Theoretic Detection of Bimanual Interactions for Dual-Arm robot Plan Generation

IEEE robotICS AND AUTOMATION LETTERS 2025年第5期10卷 4532-4539页

作者： Merlo, Elena Lagomarsino, Marta Ajoudani, Arash Ist Italiano Tecnol Human Robot Interfaces & Interact HRII Lab I-16163 Genoa Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16126 Genoa Italy

programming by demonstration is a strategy to simplify the robot programming process for non-experts via human demonstrations. However, its adoption for bimanual tasks is an underexplored problem due to the complexity of hand coordination, which also hinders data recording. This letter presents a novel one-shot method for processing a single RGB video of a bimanual task demonstration to generate an execution plan for a dual-arm robotic system. To detect hand coordination policies, we apply Shannon's information theory to analyze the information flow between scene elements and leverage scene graph properties. The generated plan is a modular behavior tree that assumes different structures based on the desired arms coordination. We validated the effectiveness of this framework through multiple subject video demonstrations, which we collected and made open-source, and exploiting data from an external, publicly available dataset. Comparisons with existing methods revealed significant improvements in generating a centralized execution plan for coordinating two-arm systems.

关键词： Hands robots robot kinematics Topology Trajectory Manipulators robot sensing systems robot programming Decision making Data mining Bimanual manipulation learning from demonstration semantic scene understanding

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Programmatic Imitation Learning From Unlabeled and Noisy Demonstrations

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IEEE robotICS AND AUTOMATION LETTERS 2024年第6期9卷 4894-4901页

作者： Xin, Jimmy Zheng, Linus Rahmani, Kia Wei, Jiayi Holtz, Jarrett Dillig, Isil Biswas, Joydeep Univ Texas Austin Austin TX 78712 USA Robert Bosch LLC Farmington Hills MI 48331 USA

Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt. There is an emerging interest in Programmatic Imitation Learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and adaptation to novel settings. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose Plunder, a novel PIL algorithm that addresses these shortcomings by synthesizing probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark Plunder against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. Plunder policies outperform the next-best baseline by 19% and 17% in matching the given demonstrations and successfully completing the tasks, respectively.

关键词： Task analysis Trajectory Probabilistic logic Noise measurement Training data Noise Approximation algorithms Computer science formal languages machine learning programming representation learning robot learning robot programming

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Hybrid and oriented harmonic potentials for safe task execution in unknown environment

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AUTOMATICA 2025年 173卷

作者： Wang, Shuaikang Guo, Meng Peking Univ Coll Engn Dept Mech & Engn Sci Beijing 100871 Peoples R China

Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it;(ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： robot programming

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Virtual elasto-plastic robot compliance to active environments

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SCIENCE robotICS 2025年第99期10卷 eadq1703页

作者： Panzirsch, Michael Singh, Harsimran Wu, Xuwei Iskandar, Maged Koepken, Anne Luz, Rute Batti, Nesrine Lay, Florian S. Manaparampil, Ajithkumar Narayanan Mayershofer, Luisa Luo, Xiaozhou Burger, Robert Bustamante-Gomez, Samuel Butterfass, Joerg den Exter, Emiel Friedl, Werner Gumpert, Thomas Pavelski, Pedro Quere, Gabriel Schmidt, Florian Albu-Schaeffer, Alin Bauer, Adrian S. Leidner, Daniel Schmaus, Peter Hagengruber, Annette Krueger, Thomas Vogel, Joern Lii, Neal Y. German Aerosp Ctr DLR Robot & Mechatron Ctr Wessling Germany European Space Agcy ESA Noordwijk Netherlands Inst Polytech Paris Palaiseau France Tech Univ Munich Sch Computat Informat & Technol Garching Germany Univ Bremen Inst Artificial Intelligence Bremen Germany

Humans exhibit a particular compliant behavior in interactions with their environment. Facilitated by fast physical reasoning, humans are able to rapidly alter their compliance, enhancing robustness and safety in active environments. Transferring these capabilities to robotics is of utmost importance particularly as major space agencies begin investigating the potential of cooperative robotic teams in space. In this scenario, robots in orbit or on planetary surfaces are meant to support astronauts in exploration, maintenance, and habitat building to reduce costs and risks of space missions. A major challenge for interactive robot teams is establishing the capability to act in and interact with dynamic environments. Analogous to humans, the robot should be not only particularly compliant in case of unexpected collisions with other systems but also able to cooperatively handle objects requiring accurate pose estimation and fast trajectory planning. Here, we show that these challenges can be attenuated through an enhancement of active robot compliance introducing a virtual plastic first-order impedance component. We present how elasto-plastic compliance can be realized via energy-based detection of active environments and how evasive motions can be enabled through adaptive plastic compliance. Two space teleoperation experiments using different robotic assets confirm the potential of the method to enhance robustness in interaction with articulated objects and facilitate robot cooperation. An experiment in a health care facility presents how the same method analogously solidifies robotic interactions in human-robot shared environments by giving the robot a subordinate role.

关键词： robot programming

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Neural Network Design for a Curved Kicking Mechanism with Obstacle Avoidance in RoboCup Small Size League (SSL)

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JOURNAL OF INTELLIGENT & robotIC SYSTEMS 2024年第3期110卷 124页

作者： Azevedo, Francisco A. B. Leao, Guilherme P. C. Maximo, Marcos R. O. A. Aeronaut Inst Technol Aeronaut Engn Div Praca Marechal Eduardo Gomes50Vila Acacias BR-12228900 Sao Jose Dos Campos SP Brazil Aeronaut Inst Technol Elect Engn Div Sao Jose Dos Campos Brazil Aeronaut Inst Technol Comp Sci Div Autonomous Computat Syst Lab LAB SCA Sao Jose Dos Campos Brazil

At the RoboCup, a robotics soccer tournament, the Small Size League (SSL) is one of its leagues. The thought of a mechanism in this league that could perform unpredictable kicks and passes inspired study into both the physical mechanism required to do it and the algorithms needed to make the most of *** introducing new ideas and utilizing a Deep Neural Network, this work contributes by improving a prior algorithm that aims to carry out a real-time inversion of the non-linear ordinary differential equation (ODE) that models the ball's path in order to determine the parameters to hit a target with a curved kick mechanism (DNN). New techniques are also presented. The two suggested DNN achieved accuracy levels of more than 92% in the outcomes of simulation runs in MATLAB.

关键词： Deep learning robot soccer robot programming Small size league RoboCup

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Exploring programming Language Choices to Boost Student Interest in Coding and Educational robotics 5th

Exploring Programming Language Choices to Boost Student Inte...

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5th International Conference on Higher Education Learning Methodologies and Technologies Online, HELMeTO 2023

作者： Negrini, Lucio Marras, Arianna Giang, Christian Babazadeh, Masiar Dipartimento formazione e apprendimento Laboratorio media e MINT Scuola universitaria professionale della Svizzera Italiana Locarno Switzerland Dipartimento di Scienze Umane Università degli studi di Salerno Filosofiche e della Formazione Salerno Italy

ISBN: (纸本)9783031673504

This research explores the impact of two widely used visual programming languages, VPL and Blockly, in educational robotics (ER) to enhance computational skills and address Science, Technology, Engineering, and Mathematics (STEM) gender disparities in K-12 education. To do so, an escape game was designed wherein children were tasked with programming a robot using either VPL or Blockly. The study gathered responses from 1635 children across 604 groups, primarily aged between 11 and 14. Statistical analysis revealed that girls tended to favour VPL more frequently than boys, while boys showed a preference for Blockly. Furthermore, participants with prior programming experience were more inclined to opt for Blockly, perceiving it as easier and more enjoyable. In sum, VPL emerged as the most utilized and preferred language, hailed for its ease of use and entertainment value compared to Blockly. These findings underscore VPL's appeal among K-12 students, particularly for girls and those lacking prior programming exposure and can be used to design learning activities and to train future teachers in ER. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： robot programming

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FluxProg 2.0 - Extended Version of a Visual Tool for Teaching programming Logic using Flowcharts with Real and Simulated robots 16

FluxProg 2.0 - Extended Version of a Visual Tool for Teachin...

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16th Brazilian Symposium on robotics and 15th Workshop on robotics in Education, SBR-WRE 2024

作者： Biscaya Gutierrez, Matheus Alberto Fabro, João Cristiano Cordeiro, André PR Curitiba Brazil DAINF-CT – UTFPR PR Curitiba Brazil

ISBN: (纸本)9798350391084

This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interface and robotics, expanding on the existing Fluxprog project. New features were implemented, such as variables, logical and mathematical operations, conditional structures, and loops, to motivate students to engage in challenges that promote logical thinking. This paper presents the design, implementation and evaluation of a programming teaching tool using a visual language based on flowcharts, with subsequent data collection through a questionnaire and analysis of preliminary results to evaluate the user’s experience while interacting with the tool. ©2024 IEEE.

关键词： robot programming

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Comparing Block-Based programming Models for Two-Armed robots

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第5期48卷 1630-1643页

作者： Ritschel, Nico Kovalenko, Vladimir Holmes, Reid Garcia, Ronald Shepherd, David C. Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada JetBrains NV JetBrains Res NL-1017 ZM Amsterdam Netherlands Virginia Commonwealth Univ Coll Engn Richmond VA 23284 USA

Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.

关键词： robot kinematics programming profession Visualization Task analysis Manipulators programming environments user interfaces robot programming parallel programming block-based programming

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Material-Driven Mechanical programming of Soft robotic Tentacles

Material-Driven Mechanical Programming of Soft Robotic Tenta...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Lu, Yao Amraouza, Asmaa Peng, Yifei Li, Dan Nassour, John Cheng, Gordon Technische Universität München Department of Electrical and Computer Engineering Munich80333 Germany

ISBN: (纸本)9798331531614

Soft robotic grippers with stochastic and topological grasping capabilities can be highly desirable for gentle contact and interaction with fragile objects of various shapes. In this study, we developed soft tentacles using silicon elastomeric material with an embedded pneumatic channel, which generates desirable three-dimensional (3D) curling deformation under pneumatic pressure. Additionally, various ratios and geometrical distributions of two silicon materials, which differ primarily in stiffness, were investigated to determine their effect on grasping efficiency. The tentacle's grasping performance was systematically tested across multiple tentacle designs and grasping strategies. Results showed that optimizing the combination of softer elastomers with stiffer materials significantly improved the tentacle's ability to securely grip and carry loads while maintaining gentle contact with objects (up to 5 kg). The pneumatic tentacle's simple control mechanism and versatility in handling objects of various shapes and sizes offer a low-cost, adaptable solution for future applications in soft robotics. © 2025 IEEE.

关键词： robot programming

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Intention-aware robot motion planning for safe worker-robot collaboration

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2024年第15期39卷 2242-2269页

作者： Liu, Yizhi Jebelli, Houtan Syracuse Univ Dept Civil & Environm Engn Syracuse NY USA Univ Illinois Dept Civil & Environm Engn Champaign IL 61820 USA

Recent advances in robotics have enabled robots to collaborate with workers in shared, fenceless workplaces in construction and civil engineering, which can improve productivity and address labor shortages. However, this collaboration may lead to collisions between workers and robots. Targeting safe collaboration, this study proposes an intention-aware motion planning method for robots to avoid collisions. This method involves two novel deep networks that allow robots to anticipate the motions of workers based on inferences about workers' motion intentions. Then, a probabilistic collision-checking mechanism is developed that enables robots to estimate the collision probability with the motions of workers and generate collision-free adjustments. The results verify that the method enables robots to predict workers' intended motions 1 s in advance and generate adjustments with a collision probability of less than 5.0% during collaborative masonry tasks. This study facilitates the safe implementation of collaborative robots in construction and civil engineering.

关键词： robot programming

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