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11th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Zappa, Isacco Fracassi, Giovanni Zanchettin, Andrea Maria Rocco, Paolo Politecn Milan Dipartimento Elettron Informaz &Bioingn Milan Italy

ISBN: (纸本)9798350315684

The rise of mass customization is leading to more frequent production changes. Being able to program robots quickly is paramount to reduce production downtimes. However, this requirement contrasts with the lack of robotic experts in industries. To solve this issue, robot manufacturers invested in developing intuitive programming interfaces for tasks commonly tackled by robots. programming by demonstration is particularly appealing for welding since it allows the robot to learn how to execute the task directly from a sample hand-guided execution of the welding trajectory. Nevertheless, solutions on the market are limited to interfaces to support the operator in saving motion waypoints. In this paper, we target the problem of parameterizing the whole welding trajectory from a single kinesthetic demonstration without the need to save waypoints. The parameterized trajectory is then translated into a sequence of suitable robot instructions for the execution of the welding. Furthermore, the paper shows how the parameterized trajectory can be extended for welding a batch of workpieces of the same shape in a predefined pattern.

关键词： programming by demonstration Hand-Guided demonstration Trajectory Parameterization Cobot Welding

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PoseVEC: Authoring Adaptive Pose-aware Effects using Visual programming and demonstrations 23

PoseVEC: Authoring Adaptive Pose-aware Effects using Visual ...

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36th Annual ACM Symposium on User Interface Software and Technology (UIST)

作者： Zhang, Yongqi Nguyen, Cuong Kazi, Rubaiat Habib Yu, Lap-Fai George Mason Univ Fairfax VA 22030 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798400701320

Pose-aware visual effects where graphics assets and animations are rendered reactively to the human pose have become increasingly popular, appearing on mobile devices, the web, or even head-mounted displays like AR glasses. Yet, creating such effects still remains difficult for novices. In a traditional video editing workflow, a creator could utilize keyframes to create expressive but non-adaptive results which cannot be reused for other videos. Alternatively, programming-based approaches allow users to develop interactive effects, but are cumbersome for users to quickly express their creative intents. In this work, we propose a lightweight visual programming workflow for authoring adaptive and expressive pose effects. By combining a programming by demonstration paradigm with visual programming, we simplify three key tasks in the authoring process: creating pose triggers, designing animation parameters, and rendering. We evaluated our system with a qualitative user study and a replicated example study, finding that all participants can create effects efficiently.

关键词： Visual efects authoring motion graphics pose recognition programming by demonstration

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From Operation to Cognition: Automatic Modeling Cognitive Dependencies from User demonstrations for GUI Task Automation 25

From Operation to Cognition: Automatic Modeling Cognitive De...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Yin, Yiwen Mei, Yu Yu, Chun Li, Toby Jia-Jun Jadoon, Aamir Khan Cheng, Sixiang Shi, Weinan Chen, Mohan Shi, Yuanchun Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering University of Notre Dame Notre Dame United States Qinghai University Qinghai Xining China

ISBN: (纸本)9798400713941

Traditional programming by demonstration (PBD) systems primarily automate tasks by recording and replaying operations on Graphical User Interfaces (GUIs), without fully considering the cognitive processes behind operations. This limits their ability to generalize tasks with interdependent operations to new contexts (e.g. collecting and summarizing introductions depending on different search keywords from varied websites). We propose TaskMind, a system that automatically identifies the semantics of operations, and the cognitive dependencies between operations from demonstrations, building a user-interpretable task graph. Users modify this graph to define new task goals, and TaskMind executes the graph to dynamically generalize new parameters for operations, with the integration of Large Language Models (LLMs). We compared TaskMind with a baseline end-to-end LLM which automates tasks from demonstrations and natural language commands, without task graph. In studies with 20 participants on both predefined and customized tasks, TaskMind significantly outperforms the baseline in both success rate and controllability. © 2025 Copyright held by the owner/author(s).

关键词： Cognitive Dependency programming by demonstration Task Automation Task Graph

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Workspace Modeling and Trajectory Mapping for Robotic Execution using Artificial Neural Networks 2

Workspace Modeling and Trajectory Mapping for Robotic Execut...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Jha, Abhishek Poorani Ayswariya, P.S. Bhattacharjee, Neelanjan Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Mechanical Engineering University of Alberta Edmonton Canada

ISBN: (纸本)9798350340235

Demonstrating a task at trajectory level is one of the key concepts in robot programming by demonstration approaches. The suitability of the demonstrated trajectory for robot execution is a challenging problem to answer in such approaches. In this work, a simplified framework is presented to judge the suitability of an externally defined trajectory for robot execution. The approach is based on learning of the workspace model of the robot and then retrieving an analogous trajectory for robot execution corresponding to externally mapped trajectory. An Artificial Neural Network architecture is used to learn the workspace model of the robot and extract an analogous trajectory. The approach is demonstrated for a six degrees of freedom robot. Simulation results reveal that the approach provides an effective way to evaluate an externally demonstrated trajectory. © 2023 IEEE.

关键词： Artificial Neural Network Kinematics programming by demonstration Robot Workspace Trajectory Mapping

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Reaching new positions using an extreme learning machine in programming by demonstration

Reaching new positions using an extreme learning machine in ...

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10th IEEE Latin American Robotics Symposium (LARS)

作者： Hoyos, Jose Prieto, Flavio Pena, Carlos Morales, Eduardo Perez-Cisneros, Marco Univ Nacl Colombia Bogota Colombia INAOE Puebla Mexico Univ Guadalajara Guadalajara Jalisco Mexico

ISBN: (纸本)9780769551395

We propose the use of the extreme learning machine in programming by demonstration. Some advantages of this technique are a fast training phase and avoiding falling in local minima. We present two ways of using it: (i) for encoding one or several trajectories of a demonstration and (ii) for learning the direct kinematic model of a robot, which once known, allows changing the final position of the demonstrated trajectory. Through comparison with other commonly used techniques, it is experimentally shown that this technique has the lowest learning time and the second lowest error. Also, using a real robot, the learning of the kinematic model was tested, reaching the final position even when this is different to the final of the demonstrated trajectory.

关键词： Neural networks Intelligent robots programming by demonstration

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Dynamic Via-points and Improved Spatial Generalization for Online Trajectory Generation with Dynamic Movement Primitives

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第1期110卷 24-24页

作者： Sidiropoulos, Antonis Doulgeri, Zoe Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization, modulation and robustness properties. However, the spatial generalization of DMP can be problematic in some cases, leading to excessive overscaling and in turn large velocities and accelerations. While other DMP variants have been proposed in the literature to tackle this issue, they can also exhibit excessive overscaling as we show in this work. Moreover, incorporating intermediate points (via-points) for adjusting the DMP trajectory to account for the geometry of objects related to the task, or to avoid or push aside objects that obstruct a specific task, is not addressed by the current DMP literature. In this work we tackle these unresolved so far issues by proposing an improved online spatial generalization, that remedies the shortcomings of the classical DMP generalization, and moreover allows the incorporation of dynamic via-points. This is achieved by designing an online adaptation scheme for the DMP weights which is proved to minimize the distance from the demonstrated acceleration profile to retain the shape of the demonstration, subject to dynamic via-point and initial/final state constraints. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the practical usefulness and efficiency of the proposed method.

关键词： Dynamic movement primitives Via-points programming by demonstration Trajectory generation

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Experimental Evaluation Of Intuitive programming Of Robot Interaction Behaviour During Kinesthetic Teaching Using sEMG And Cutaneous Feedback 13

Experimental Evaluation Of Intuitive Programming Of Robot In...

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13th IFAC Symposium on Robot Control (SYROCO)

作者： Meattini, Roberto Chiaravalli, Davide Galassi, Kevin Palli, Gianuca Melchiorri, Claudio Univ Bologna Dept Elect Elect & Informat Engn DEI Viale Risorgimento 2 I-40136 Bologna Italy

ISBN: (纸本)9781713867890

Modern applications related to service or production can nowadays benefit from the introduction of collaborative robots already available on the market and endowed with several advanced features such as precise torque control or safe physical interaction with operators. More importantly, collaborative robots allow operators to teach end-effector trajectories by means of physical interaction known as kinesthetic teaching which is one of the most intuitive programming-by-demonstration techniques. However, important functionalities provided by modern collaborative robots, like the possibility of performing smooth interactions, cannot be programmed intuitively with the available framework of kinesthetic teaching. In the present study, we propose and experimentally evaluate a robot programming framework for the simultaneous teaching of both trajectories by means of kinesthetic teaching, and robot interaction behavior by means of impedance shaping along the trajectory exploiting a wearable interface. Specifically, the wearable interface is designed to not affect the free motion of the operator, necessary to perform kinesthetic teaching, and it is based on the usage of surface electromyography (EMG) and vibrotactile stimulation. In this way, we propose an intuitive robot programming framework for an offline robot trajectory and interaction behavior programming, according to which the operator will be able to plan interactions with the environment and humans. In this article, we report a preliminary experimental evaluation of the proposed system, in which an operator will teach a 7-degrees-of-freedom manipulator the execution of a task on a robotic wiring test-bed. In the experiment, the programming of requested compliance levels during the kinesthetic teaching of a trajectory is performed, and the reported results show that the provided wearable interface is successfully exploited by the operator. Finally, the experiment also demonstrates that the offline intuitive program

关键词： Human-centered automation human-robot interaction programming by demonstration

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A Bayesian tracker for synthesizing mobile robot behaviour from demonstration

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AUTONOMOUS ROBOTS 2021年第8期45卷 1077-1096页

作者： Magnenat, Stephane Colas, Francis Swiss Fed Inst Technol Game Technol Ctr Zurich Switzerland Univ Lorraine LORIA Inria CNRS F-54000 Nancy France

programming robots often involves expert knowledge in both the robot itself and the task to execute. An alternative to direct programming is for a human to show examples of the task execution and have the robot perform the task based on these examples, in a scheme known as learning or programming from demonstration. We propose and study a generic and simple learning-from-demonstration framework. Our approach is to combine the demonstrated commands according to the similarity between the demonstrated sensory trajectories and the current replay trajectory. This tracking is solely performed based on sensor values and time and completely dispenses with the usually expensive step of precomputing an internal model of the task. We analyse the behaviour of the proposed model in several simulated conditions and test it on two different robotic platforms. We show that it can reproduce different capabilities with a limited number of meta parameters.

关键词： programming by demonstration Learning from demonstration Non-parametric Bayesian model Teach and repeat Online tracking

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Robust Segmentation and Classification of Robot Skills from demonstrations with Detection of Unknown Skills

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Procedia Computer Science 2025年 253卷 1535-1544页

作者： Isacco Zappa Alessandro Tomasoni Andrea Maria Zanchettin Paolo Rocco Politecnico di Milano Piazza Leonardo da Vinci Milano 20133 Italy Bettinelli S.p.A Via Leonardo da Vinci 56 Bagnolo Cremasco 26010 Italy

The shift towards more customizable products drives small and medium-sized enterprises to adopt flexible production systems. The ability to quickly reconfigure robots for new tasks is crucial, highlighting the need for easy programming tools due to the scarcity of robotics experts. This paper presents a segmentation and classification method to autonomously generate from a kinesthetic demonstration the sequence of robot skills required to deploy the robot for a task. The approach begins by describing the environment using predicates and defining robot skills by their preconditions and effects. demonstration segmentation is achieved by detecting transitions in the world state, and segments are classified using a cyclic skill-guessing algorithm. The method is validated with a skill library that includes manipulation and tool skills such as welding and polishing. Results show performance comparable to the baseline with improved robustness to missed predicate activations and the ability to identify missing skills, prompting operator intervention.

关键词： programming by demonstration Symbolic AI Collaborative Robots Hand-Guided demonstrations

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Morphing Based Transfer of Demonstrated Surface Finishing Trajectories to Point Clouds of Similar Objects

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Procedia Computer Science 2025年 253卷 1002-1011页

作者： Philipp Möhl Anish Pratheepkumar Markus Ikeda Andreas Pichler Profactor GmbH Im Stadtgut D1 AT-4407 Steyr Austria

For production in small lot sizes, typical for SMEs, efficient and intuitive programming of process trajectories or transferring them to similar objects is important, particularly for robotic applications such as polishing where frequent reprogramming is needed. Existing approaches rely on CAD models or large training datasets for enabling trajectory transfer. In contrast, this research explores a novel algorithm that facilitate direct transfer of expert demonstrated trajectories between objects represented as point clouds from 3D scans. To achieve performance even without prior object knowledge, we use a neural network-based non-linear model, typically used in animation, to enable morphing of a source to a target point cloud which inherently transforms the associated process trajectories along with it. We address the primary challenge of differences in point clouds by introduction of a keypoint-driven loss function, which aids the model by preserving the local point distribution and thus ensuring accurate trajectory transformation. This innovation avoids the need for any prior training, offering trajectory generation also for new object classes directly at inference. The performance of this algorithm is benchmarked against traditional interpolation methods, demonstrating its superiority in generating trajectories and applicability in industrial settings. This is a significant step forward in adaptive robotic operations, reducing the dependency on extensive datasets and enhancing the flexibility and efficiency of smart manufacturing systems.

关键词： human robot collaboration programming by demonstration robotic surface processing trajectory morphing

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