检索结果-内蒙古大学图书馆

An image-based trajectory planning approach for robust robot programming by demonstration

ROBOTICS AND AUTONOMOUS SYSTEMS 2017年 98卷 241-257页

作者： Vakanski, Aleksandar Janabi-Sharifi, Farrokh Mantegh, Iraj Univ Idaho Ind Technol 1776 Sci Ctr Dr Idaho Falls ID 83402 USA Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada Natl Res Council Canada Inst Aerosp Res NRC IAR Montreal PQ Canada

The article proposes a new robot programming-by-demonstration framework, which integrates a visual servoing tracking control to robustly follow a trajectory generated from observed demonstrations. The constraints originating from the use of a visual servoing controller are incorporated into the trajectory learning phase, to guarantee feasibility of the generated plan for task execution. The observational learning is solved as a constrained optimization problem, with an objective to generalize from a set of trajectories of salient features in the image space of a vision camera. The proposed approach is evaluated experimentally for learning trajectories acquired from kinesthetic demonstrations. (C) 2017 Published by Elsevier B.V.

关键词： Cognitive robotics Visual servoing programming by demonstration

来源：评论

学校读者我要写书评

暂无评论

Data generation and representation method for 3D video conferencing using programming by demonstration

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2013年第1期67卷 71-95页

作者： Sung, Yunsick Cho, Kyungeun Dongguk Univ Grad Sch Dept Game Engn Seoul 100715 South Korea Dongguk Univ Dept Multimedia Engn Seoul 100715 South Korea

Video conferencing provides an environment for multiple users linked on a network to have meetings. Since a large quantity of audio and video data are transferred to multiple users in real time, research into reducing the quantity of data to be transferred has been drawing attention. Such methods extract and transfer only the features of a user from video data and then reconstruct a video conference using virtual humans. The disadvantage of such an approach is that only the positions and features of hands and heads are extracted and reconstructed, whilst the other virtual body parts do not follow the user. In order to enable a virtual human to accurately mimic the entire body of the user in a 3D virtual conference, we examined what features should be extracted to express a user more clearly and how they can be reproduced by a virtual human. This 3D video conferencing estimates the user's pose by comparing predefined images with a photographed user's image and generates a virtual human that takes the estimated pose. However, this requires predefining a diverse set of images for pose estimation and, moreover, it is difficult to define behaviors that can express poses correctly. This paper proposes a framework to automatically generate the pose-images used to estimate a user's pose and the behaviors required to present a user using a virtual human in a 3D video conference. The method for applying this framework to a 3D video conference on the basis of the automatically generated data is also described. In the experiment, the framework proposed in this paper was implemented in a mobile device. The generation process of poses and behaviors of virtual human was verified. Finally, by applying programming by demonstration, we developed a system that can automatically collect the various data necessary for a video conference directly without any prior knowledge of the video conference system.

关键词： Video conferencing programming by demonstration Maximin Selection Algorithm Virtual human Behavior generation

来源：评论

学校读者我要写书评

暂无评论

Applying machine learning to programming by demonstration

引用

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2004年第3期16卷 161-188页

作者： Paynter, GW Witten, IH Univ Calif Riverside Riverside CA 92521 USA Univ Waikato Hamilton New Zealand

Familiar' is a tool that helps end-users automate iterative tasks in their applications by showing examples of what they want to do. It observes the user's actions, predicts what they will do next, and then offers to complete their task. Familiar learns in two ways. First, it creates a model, based on data gathered from training tasks, that selects the best prediction from among several candidates. Experiments show that decision trees outperform heuristic methods, and can be further improved by incrementally updating the classifier at task time. Second, it uses decision stumps inferred from analogous examples in the event trace to predict the parameters of conditional rules. Because data is sparse - for most users balk at giving more than a few training examples - permutation tests are used to calculate the statistical significance of each stump, successfully eliminating bias towards attributes with many different values.

关键词： programming by demonstration adaptive user interfaces human computer interaction machine learning iterative tasks

来源：评论

学校读者我要写书评

暂无评论

Constructing task-level assembly strategies in robot programming by demonstration

引用

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2005年第12期24卷 1073-1085页

作者： Chen, JR Australian Natl Univ Dept Informat Engn Res Sch Informat Sci & Engn Canberra ACT Australia

programming by demonstration (PbD) is a technique for programming robots that holds much promise in making robots more accessible to ordinary, non-technical users. However a well-known difficulty with the method is that a human will often demonstrate the task to be programmed inconsistently or even erroneously, leading to the inclusion of what is essentially noise in the demonstration. A number of techniques exist in the literature for filtering out this hype of noise;however most focus oil very low level control command details. In this paper we propose a new, complementary direction of research. We take a "task-level" view of the demonstration, and note that noise call exist at this level also. We propose a framework, based on a hybrid dynamic system modeling approach, to select the most optimal, task-level execution strategies that were demonstrated. We apply our framework to a real household task of inserting the compressible spindle of a paper towel holder into its supports. We conduct experiments to show that significant improvements in robot performance of the task call be achieved by a PbD regime that includes our method.

关键词： programming by demonstration teaching by showing hybrid dynamic systems task level planning

来源：评论

学校读者我要写书评

暂无评论

A Method for Learning Macro-Actions for Virtual Characters Using programming by demonstration and Reinforcement Learning

引用

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2012年第3期8卷 409-420页

作者： Sung, Yunsick Cho, Kyungeun Dongguk Univ Seoul Grad Sch Dept Game Engn Grad Seoul South Korea Dongguk Univ Seoul Dept Multimedia Engn Seoul South Korea

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through programming by demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

关键词： Reinforcement Learning Monte Carlo Method Behavior Generation Model programming by demonstration Macro-Action Multi-Step Action

来源：评论

学校读者我要写书评

暂无评论

Design and Optimization of a BCI-Driven Telepresence Robot Through programming by demonstration

引用

IEEE ACCESS 2019年 7卷 111625-111636页

作者： Abibullaev, Berdakh Zollanvari, Amin Saduanov, Batyrkhan Alizadeh, Tohid Nazarbayev Univ Dept Robot & Mechatron Nur Sultan 010000 Kazakhstan Nazarbayev Univ Dept Elect & Comp Engn Nur Sultan 010000 Kazakhstan

Improving the life quality of people with severe motor paralysis has a significant impact on restoring their functional independence to perform activities of daily living (ADL). Telepresence is a subfield of the robotic-assisted route, where human plays the role of an operator, sending high-level instructions to an as sistive robot while receiving sensory feedback. However, for severely motor-impaired people, conventional interaction modalities may not be suitable due to their complete paralysis. Thus, designing alternative ways of interaction such as Brain-Computer Interfaces (BCI) is essential for a telepresence capability. We propose a novel framework that integrates a BCI system and a humanoid robot to develop a brain-controlled telepresence system with multimodal control features. In particular, the low-level control is executed by programming by demonstration (PbD) models, and the higher-level cognitive commands are produced by a BCI system to perform vital ADLs. The presented system is based on real-time decoding of attention-modulated neural responses elicited in the brain electroencephalographic signals and generating multiple control commands. As a result, the system allows a user to interact with a humanoid robot while receiving auditory and visual feedback from the robot's sensors. We validated our system across ten subjects in a realistic scenario. The experimental results show the feasibility of the approach in the design of a telepresence robot with high BCI decoding performances.

关键词： Brain-Computer interfaces telepresence programming by demonstration EEG event-related potentials humanoid robots

来源：评论

学校读者我要写书评

暂无评论

Robot programming by demonstration with a monocular RGB camera

引用

INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2023年第2期50卷 234-245页

作者： Wang, Kaimeng Tang, Te FANUC Amer Corp FANUC Adv Res Lab Union City CA 94587 USA

Purpose This paper aims to present a new approach for robot programming by demonstration, which generates robot programs by tracking 6 dimensional (6D) pose of the demonstrator's hand using a single red green blue (RGB) camera without requiring any additional sensors. Design/methodology/approach The proposed method learns robot grasps and trajectories directly from a single human demonstration by tracking the movements of both human hands and objects. To recover the 6D pose of an object from a single RGB image, a deep learning-based method is used to detect the keypoints of the object first and then solve a perspective-n-point problem. This method is first extended to estimate the 6D pose of the nonrigid hand by separating fingers into multiple rigid bones linked with hand joints. The accurate robot grasp can be generated according to the relative positions between hands and objects in the 2 dimensional space. Robot end-effector trajectories are generated from hand movements and then refined by objects' start and end positions. Findings Experiments are conducted on a FANUC LR Mate 200iD robot to verify the proposed approach. The results show the feasibility of generating robot programs by observing human demonstration once using a single RGB camera. Originality/value The proposed approach provides an efficient and low-cost robot programming method with a single RGB camera. A new 6D hand pose estimation approach, which is used to generate robot grasps and trajectories, is developed.

关键词： programming by demonstration Hand pose tracking Grasp teaching by demonstration

来源：评论

学校读者我要写书评

暂无评论

A spatial information inference method for programming by demonstration of assembly tasks by integrating visual observation with CAD model

引用

ASSEMBLY AUTOMATION 2020年第5期40卷 689-701页

作者： Zhou, Zhongxiang Ji, Liang Xiong, Rong Wang, Yue Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China

Purpose In robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from enough to ensure a successful execution. This paper aims to develop an inference method to improve the accuracy of poses and assembly relations between parts by integrating visual observation with computer-aided design (CAD) model. Design/methodology/approach In this paper, the authors propose a spatial information inference method called probabilistic assembly graph with optional CAD model, shorted as PAGC*, to achieve this task. Then an assembly relation extraction method from CAD model is designed, where different assembly relation descriptions in CAD model are summarized into two fundamental relations that are colinear and coplanar. The relation similarity, distance similarity and rotation similarity are adopted as the similar part matching criterions between the CAD model and the observation. The knowledge of part in CAD is used to correct that of the corresponding part in observation. The likelihood maximization estimation is used to infer the accurate poses and assembly relations based on the probabilistic assembly graph. Findings In the experiments, both simulated data and real-world data are applied to evaluate the performance of the PAGC* model. The experimental results show the superiority of PAGC* in accuracy compared with assembly graph (AG) and probabilistic assembly graph without CAD model (PAG). Originality/value The paper provides a new approach to get the accurate pose of each part in demonstration stage of the robot PbD system. By integrating information from visual observation with prior knowledge from CAD model, PAGC* ensures the success in execution stage of the PbD system.

关键词： Assembly tasks CAD model programming by demonstration Spatial information inference

来源：评论

学校读者我要写书评

暂无评论

ROBOT programming by demonstration FOR TASK IN CONTACT STATE

引用

Chinese Journal of Mechanical Engineering 2004年第3期17卷 336-339页

作者： WangQin QianJun WangChunxiang YangRuqing ResearchInstituteofRobotics ShanghaiJiaotongUniversityShanghai200030China

Robot programming by demonstration (PBD) system for task in which objectrequires contact with environment is built based on the controlling skill model. The skill isdescribed in three aspects: contact state classifier, acquirement of contact states sequence andcontrolling transition between states. The classifier is developed with the support vector machineby using force sense. Sequence of states is obtained from the force signal of demonstration by theevent trigger. The velocity command of transition is achieved by linearization method. The PBDsystem is successfully built with robot controller with open architecture.

关键词： Robot programming by demonstration Contact state Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Leveraging on a virtual environment for robot programming by demonstration

引用

ROBOTICS AND AUTONOMOUS SYSTEMS 2004年第2-3期47卷 153-161页

作者： Aleotti, J Caselli, S Reggiani, M Univ Parma Dipartimento Ingn Informaz I-43100 Parma Italy

The programming by demonstration paradigm promises to reduce the complexity of robot programming. Its aim is to let robot systems learn new behaviors from a human operator demonstration. In this paper, we argue that while providing demonstrations in the real environment enables teaching of general tasks, for tasks whose essential features are known a priori demonstrating in a virtual environment may improve efficiency and reduce trainer's fatigue. We next describe a prototype system supporting programming by demonstration in a virtual environment and we report results obtained exploiting simple virtual tactile fixtures in pick-and-place tasks. (C) 2004 Elsevier B.V. All rights reserved.

关键词： robot programming programming by demonstration virtual fixtures virtual environments

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：