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ACM TRANSACTIONS ON THE WEB 2011年第3期5卷 1–45页

作者： Tuchinda, Rattapoom Knoblock, Craig A. Szekely, Pedro Natl Elect & Comp Technol Ctr Klongluang 12120 Pathumthani Thailand Univ So Calif Los Angeles CA 90089 USA

The latest generation of WWW tools and services enables Web users to generate applications that combine content from multiple sources. This type of Web application is referred to as a mashup. Many of the tools for constructing mashups rely on a widget paradigm, where users must select, customize, and connect widgets to build the desired application. While this approach does not require programming, the users must still understand programming concepts to successfully create a mashup. As a result, they are put off by the time, effort, and expertise needed to build a mashup. In this article, we describe our programming-by-demonstration approach to building mashup by example. Instead of requiring a user to select and customize a set of widgets, the user simply demonstrates the integration task by example. Our approach addresses the problems of extracting data from Web sources, cleaning and modeling the extracted data, and integrating the data across sources. We implemented these ideas in a system called Karma, and evaluated Karma on a set of 23 users. The results show that, compared to other mashup construction tools, Karma allows more of the users to successfully build mashups and makes it possible to build these mashups significantly faster compared to using a widget-based approach.

关键词： Algorithms Design Human Factors Performance Mashups information integration programming by demonstration

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Guided latent space regression for human motion generation

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ROBOTICS AND AUTONOMOUS SYSTEMS 2013年第4期61卷 340-350页

作者： Avizzano, Carlo Alberto Scuola Super Sant Anna Inst Telecommun Informat Technol & Perceptual Rob PERCRO Lab Pisa Italy

In the present work, we describe a mathematical model to generate human-like motion trajectories in space. We use linear regression in a latent space to find the model parameters from a set of demonstration examples. The learning procedure requires a relevant set of similar examples. The apprehended models encode both the typical shapes of motion and their variability towards specific boundary conditions (BC). We will show the added value of encoding both properties in a unique model and we apply this ability to common problems of error compensation and target tracking. The models allow us to describe human motion using expansion-function series (EFS), thus avoiding typical stability issues that arise in the use of differential equation models. To cope with variable scenarios, we show two specific algorithms that morph and adapt the evolution trajectory. In analogy to splines, the EFS preserve an analytical structure on which we develop the optimisation steps. In such a way, we managed to combine multiple single segments into complex motions that preserve continuity and may simultaneously optimise other criteria. In the present work, after having analysed similar tools, we present the basic model and its features. Then we develop a robust tool to gather the model from examples, and to achieve real-time trajectory adaptation. The achieved results will be analysed through an experimental analysis on data collected in a ball catching experiment. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Motion models Trajectory generation programming by demonstration Robust regression Trajectory adaptation

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A friend for assisting handicapped people

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2001年第1期8卷 57-65页

作者： Martens, C Ruchel, N Lang, O Ivlev, O Gräser, A Univ Bremen Inst Automat Dept Proc Automat & Robot D-28359 Bremen Germany Univ Groningen Dept Comp Sci NL-9700 AB Groningen Netherlands

The Semiautonomous Robotic System "FRIEND" consists of an electric wheelchair with a robotic arm and utilizes a speech interface.

关键词： rehabilitation robots speech-control interface redundant robots skill-based autonomous control programming by demonstration

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HILC: Domain-Independent PbD System Via Computer Vision and Follow-Up Questions

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2019年第2-3期9卷 1–27页

作者： Intharah, Thanapong Turmukhambetov, Daniyar Brostow, Gabriel J. UCL Gower St London WC1E 6BT England Khon Kaen Univ 123 Mittraphap Rd Khon Kaen 40002 Thailand

Creating automation scripts for tasks involving Graphical User Interface (GUI) interactions is hard. It is challenging because not all software applications allow access to a program's internal state, nor do they all have accessibility APIs. Although much of the internal state is exposed to the user through the GUI, it is hard to programmatically operate the GUI's widgets. To that end, we developed a system prototype that learns by demonstration, called HILC (Help, It Looks Confusing). Users, both programmers and non-programmers, train HILC to synthesize a task script by demonstrating the task. A demonstration produces the needed screenshots and their corresponding mouse-keyboard signals. After the demonstration, the user answers follow-up questions. We propose a user-in-the-loop framework that learns to generate scripts of actions performed on visible elements of graphical applications. Although pure programming by demonstration is still unrealistic due to a computer's limited understanding of user intentions, we use quantitative and qualitative experiments to show that non-programming users are willing and effective at answering follow-up queries posed by our system, to help with confusing parts of the demonstrations. Our models of events and appearances are surprisingly simple but are combined effectively to cope with varying amounts of supervision. The best available baseline, Sikuli Slides, struggled to assist users in the majority of the tests in our user study experiments. The prototype with our proposed approach successfully helped users accomplish simple linear tasks, complicated tasks (monitoring, looping, and mixed), and tasks that span across multiple applications. Even when both systems could ultimately perform a task, ours was trained and refined by the user in less time.

关键词： programming by demonstration GUI automation action segmentation and recognition visual-based programming by demonstration

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Socially guided intrinsic motivation for robot learning of motor skills

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AUTONOMOUS ROBOTS 2014年第3期36卷 273-294页

作者： Sao Mai Nguyen Oudeyer, Pierre-Yves INRIA Flowers Team Paris France ENSTA ParisTech Paris France

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our algorithmic architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line, we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

关键词： Active learning Intrinsic motivation Exploration Motor skill learning Inverse model programming by demonstration Learning from demonstration Imitation

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Can Human-Inspired Learning Behaviour Facilitate Human-Robot Interaction?

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INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS 2020年第1期12卷 173-186页

作者： Carfi, Alessandro Villalobos, Jessica Coronado, Enrique Bruno, Barbara Mastrogiovanni, Fulvio Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 13 I-16145 Genoa Italy

The evolution of production systems for smart factories foresees a tight relation between human operators and robots. Specifically, when robot task reconfiguration is needed, the operator must be provided with an easy and intuitive way to do it. A useful tool for robot task reconfiguration is programming by demonstration (PbD). PbD allows human operators to teach a robot new tasks by showing it a number of examples. The article presents two studies investigating the role of the robot in PbD. A preliminary study compares standard PbD with human-human teaching and suggests that a collaborative robot should actively participate in the teaching process as human practitioners typically do. The main study uses a wizard of oz approach to determine the effects of having a robot actively participating in the teaching process, specifically by controlling the end-effector. The results suggest that active behaviour inspired by humans can lead to a more intuitive PbD.

关键词： programming by demonstration Kinesthetic teaching Human robot interaction Industry 4 0

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Skill learning and action recognition by arc-length dynamic movement primitives

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ROBOTICS AND AUTONOMOUS SYSTEMS 2018年 100卷 225-235页

作者： Gaspar, Timotej Nemec, Bojan Morimoto, Jun Ude, Aleg Jozef Stefan Inst Dept Automat Biocybernet & Robot Humanoid & Cognit Robot Lab Jamova Cesta 39 Ljubljana 1000 Slovenia ATR Computat Neurosci Labs Dept Brain Robot Interface 2-2-2 HikaridaiSeika Cho Kyoto 6190288 Japan

Effective robot programming by demonstration requires the availability of multiple demonstrations to learn about all relevant aspects of the demonstrated skill or task. Typically, a human teacher must demonstrate several variants of the desired task to generate a sufficient amount of data to reliably learn it. Here a problem often arises that there is a large variability in the speed of execution across human demonstrations. This can cause problems when multiple demonstrations are compared to extract the relevant information for learning. In this paper we propose an extension of dynamic movement primitives called arc-length dynamic movement primitives, where spatial and temporal components of motion are well separated. We show theoretically and experimentally that the proposed representation can be effectively applied for robot skill learning and action recognition even when there are large variations in the speed of demonstrated movements. (C) 2017 Elsevier B.V. All rights reserved.

关键词： programming by demonstration Skill learning Action recognition Dynamic movement primitives

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Improv: An Input Framework for Improvising Cross-Device Interaction by demonstration

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ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION 2017年第2期24卷 1–21页

作者： Chen, Xiang 'Anthony' Li, Yang Carnegie Mellon Univ Human Comp Interact Inst 5000 Forbes Ave Pittsburgh PA 15213 USA Google Res 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

As computing devices become increasingly ubiquitous, it is now possible to combine the unique capabilities of different devices or Internet of Things to accomplish a task. However, there is currently a high technical barrier for creating cross-device interaction. This is especially challenging for end users who have limited technical expertise-end users would greatly benefit from custom cross-device interaction that best suits their needs. In this article, we present Improv, a cross-device input framework that allows a user to easily leverage the capability of additional devices to create new input methods for an existing, unmodified application, e.g., creating custom gestures on a smartphone to control a desktop presentation application. Instead of requiring developers to anticipate and program these cross-device behaviors in advance, Improv enables end users to improvise them on the fly by simple demonstration, for their particular needs and devices at hand. We showcase a range of scenarios where Improv is used to create a diverse set of useful cross-device input. Our study with 14 participants indicated that on average it took a participant 10 seconds to create a cross-device input technique. In addition, Improv achieved 93.7% accuracy in interpreting user demonstration of a target UI behavior by looking at the raw input events from a single example.

关键词： Cross-device interaction end-user programming programming by demonstration gesture-based interaction input framework

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Integrating user interface agents with conventional applications

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KNOWLEDGE-BASED SYSTEMS 1998年第1期11卷 15-23页

作者： Lieberman, H MIT Media Lab Cambridge MA 02139 USA

In most experiments with user interface agents to date, it has been necessary either to implement both the agent and the application from scratch, or to modify the code of an existing application to enable the necessary communication. Instead, we would like to be able to 'attach' an agent to an existing application, while requiring only a minimum of advance planning on the part of the application developer. Commercial applications an increasingly supporting the use of 'application programmers' interfaces' and scripting languages as means of achieving external control of applications. Are these mechanisms sufficient for software agents to achieve communication with applications? This paper reports some preliminary experiments in developing agent software that works with existing, unmodified commercial applications and agents that work across multiple applications. We describe a programming by example agent, ScriptAgent, that uses a scripting language, Applescript, to record example procedures that are generalized by the agent. Another approach is examinability, where the application grants to the agent the right to examine internal data structures. We present another kind of learning agent, Tatlin, that compares successive application states to infer interface operations. Finally, we discuss broader systems issues such as parallelism, interface sharing between agent and application, and access to objects. (C) 1998 Published by Elsevier Science B.V. All rights reserved.

关键词： agents scripting languages programming by example programming by demonstration machine learning user interface

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Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations

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ROBOTICS AND AUTONOMOUS SYSTEMS 2015年 74卷 97-107页

作者： Manschitz, Simon Kober, Jens Gienger, Michael Peters, Jan Tech Univ Darmstadt Inst Intelligent Autonomous Syst D-64289 Darmstadt Germany Honda Res Inst Europe D-63073 Offenbach Germany Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

We present an approach for learning sequential robot skills through kinesthetic teaching. In our work, finding the transitions between consecutive movement primitives is treated as multiclass classification problem. We show how the goal parameters of linear attractor movement primitives can be learned from manually segmented and labeled demonstrations and how the observed movement primitive order can help to improve the movement reproduction. The improvement is achieved by restricting the classification result to the currently activated movement primitive and its possible successors in a graph representation of the sequence, which is also learned from the demonstrations. The approach is validated with three experiments using a Barrett WAM robot. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Human-robot interaction Kinesthetic teaching Learning from demonstration programming by demonstration

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