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demonstration-BASED BEHAVIOR programming FOR EMBODIED VIRTUAL AGENTS

COMPUTATIONAL INTELLIGENCE 2008年第4期24卷 235-256页

作者： Dinerstein, Jonathan Egbert, Parris K. Ventura, Dan Goodrich, Michael Brigham Young Univ Dept Comp Sci Provo UT 84602 USA

We present a novel technique for behavioral animation through data-driven behavior synthesis. This technique has two key features: it provides natural character behavior and has a programming-by-demonstration interface. Thus we can quickly create compelling autonomous virtual agents that exhibit stylized behavior. First, the human user demonstrates behavior for the character by specifying its high-level actions (e.g., with a joystick) during an interactive session. Each demonstration is recorded as a sequence of discrete actions. Later, the character synthesizes novel behavior by concatenating segments of action sequences. The choice of segments is guided by simulations that predict fitness. Thus our technique operates such as a cognitive model, providing a character with deliberative decision making. The actions are abstract and can be mapped to any pertinent motions, even procedurally synthesized motions. Thus our technique complements character animation algorithms. We empirically show that our O(log n) technique is scalable, robust when provided with sufficient data, produces effective behavior for a number of problem domains, and is faster than traditional planning. Also, the interface is intuitive enough that character behavior can be created by nontechnical users.

关键词： behavioral animation cognitive modeling programming by demonstration machine learning embodied virtual agents AI-based animation

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Design and Validation of a 3-Wire demonstration Device for programming Industrial Manipulator

Design and Validation of a 3-Wire Demonstration Device for P...

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Michael, Milan Rahul, E. S. Bhavani, Rao R. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn AMMACHI Labs Amritapuri India

ISBN: (纸本)9781509061068

programming by demonstration (PbD) is an intuitive way of teaching new skills to a robot either by the guidance of demonstration devices or by means of natural communications, such as gestures, voice commands, and kinesthetic methods. This paper proposes a novel low-cost demonstration device for an industrial manipulator that can considerably reduce time and effort required for programming. The demonstration device is designed to be portable and can be easily integrated to an existing industrial manipulator. In a repeatability test, the robustness of the device is validated to meet the industrial standards. The device is integrated to an ABB industrial manipulator and the functionality is verified for a pattern tracing task.

关键词： Industrial Manipulator programming by demonstration Task programming/Teaching Device Trilateration Teleoperation

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programming AN INDUSTRIAL ROBOT BY demonstration

PROGRAMMING AN INDUSTRIAL ROBOT BY DEMONSTRATION

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23rd International DAAAM Symposium on Intelligent Manufacturing and Automation - Focus on Sustainability

作者： Stipancic, Tomislav Jerbic, Bojan Bucevic, Ante Curkovic, Petar

ISBN: (纸本)9783901509919

The constant need for reducing production costs and faster arrival tothe market of new products requires changes in product design, material selection, and production processes. While applying the changes, a level of complexity of contemporary industrial systems is undoubtedly increasing. Despite the fact that industrial robotic systems are becoming more and more complex, they should become user-friendlier and easily accessible for human operators at the same time. Such systems should have a smaller number of welltrained and highly educated employees who manage the production. This paper present an approach in programming a robot for doing assembly tasks based on programming by demonstration principles. The system tracks gestures and movements of the human operator by using Microsoft Kinect r sensor. To enable an interaction between the system and the human operatora simple gesture command list is developed. This methodology could help non-expert users to teach robots how to perform new assembly tasks in more human like ways. The command list present a very first step in a development of more powerfulgesture language that should provide a simple and an intuitive way forexchaniging information between people and machines.

关键词： programming by demonstration context awareness machine vision cognitive robotics

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Spatial programming for Industrial Robots Through Task demonstration

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第5期10卷

作者： Lambrecht, Jens Kleinsorge, Martin Rosenstrauch, Martin Krueger, Joerg Berlin Inst Technol Inst Machine Tools & Factory Management IWF Dept Ind Automat Technol Berlin Germany Fraunhofer Inst Prod Syst & Design Technol IPK Berlin Germany

We present an intuitive system for the programming of industrial robots using markerless gesture recognition and mobile augmented reality in terms of programming by demonstration. The approach covers gesture-based task definition and adaption by human demonstration, as well as task evaluation through augmented reality. A 3D motion tracking system and a handheld device establish the basis for the presented spatial programming system. In this publication, we present a prototype toward the programming of an assembly sequence consisting of several pick-and-place tasks. A scene reconstruction provides pose estimation of known objects with the help of the 2D camera of the handheld. Therefore, the programmer is able to define the program through natural bare-hand manipulation of these objects with the help of direct visual feedback in the augmented reality application. The program can be adapted by gestures and transmitted subsequently to an arbitrary industrial robot controller using a unified interface. Finally, we discuss an application of the presented spatial programming approach toward robot-based welding tasks.

关键词： Industrial Robot Augmented Reality Gestures programming by demonstration Object Recognition

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Robot programming from a Single demonstration for High Precision Industrial Insertion

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SENSORS 2023年第5期23卷 2514-2514页

作者： Wang, Kaimeng Fan, Yongxiang Sakuma, Ichiro FANUC Amer Corp FANUC Adv Res Lab Union City CA 94587 USA Univ Tokyo Dept Precis Engn Tokyo 1138654 Japan

We propose a novel approach for robotic industrial insertion tasks using the programming by demonstration technique. Our method allows robots to learn a high-precision task by observing human demonstration once, without requiring any prior knowledge of the object. We introduce an Imitated-to-Finetuned approach that generates imitated approach trajectories by cloning the human hand's movements and then fine-tunes the goal position with a visual servoing approach. To identify features on the object used in visual servoing, we model object tracking as the moving object detection problem, separating each demonstration video frame into the moving foreground that includes the object and demonstrator's hand and the static background. Then a hand keypoints estimation function is used to remove the redundant features on the hand. The experiment shows that the proposed method can make robots learn precision industrial insertion tasks from a single human demonstration.

关键词： programming by demonstration robotic insertion visual servoing line matching

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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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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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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

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

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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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Coupled dynamical system based arm-hand grasping model for learning fast adaptation strategies

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ROBOTICS AND AUTONOMOUS SYSTEMS 2012年第3期60卷 424-440页

作者： Shukla, Ashwini Billard, Aude Ecole Polytech Fed Lausanne Learning Algorithms & Syst Lab LASA Lausanne Switzerland

Performing manipulation tasks interactively in real environments requires a high degree of accuracy and stability. At the same time, when one cannot assume a fully deterministic and static environment, one must endow the robot with the ability to react rapidly to sudden changes in the environment. These considerations make the task of reach and grasp difficult to deal with. We follow a programming by demonstration (PbD) approach to the problem and take inspiration from the way humans adapt their reach and grasp motions when perturbed. This is in sharp contrast to previous work in PbD that uses unperturbed motions for training the system and then applies perturbation solely during the testing phase. In this work, we record the kinematics of arm and fingers of human subjects during unperturbed and perturbed reach and grasp motions. In the perturbed demonstrations, the target's location is changed suddenly after the onset of the motion. Data show a strong coupling between the hand transport and finger motions. We hypothesize that this coupling enables the subject to seamlessly and rapidly adapt the finger motion in coordination with the hand posture. To endow our robot with this competence, we develop a coupled dynamical system based controller, whereby two dynamical systems driving the hand and finger motions are coupled. This offers a compact encoding for reach-to-grasp motions that ensures fast adaptation with zero latency for re-planning. We show in simulation and on the real iCub robot that this coupling ensures smooth and "human-like" motions. We demonstrate the performance of our model under spatial, temporal and grasp type perturbations which show that reaching the target with coordinated hand-arm motion is necessary for the success of the task. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Grasping Hand-arm coordination Fast perturbations Manipulation planning programming by demonstration

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