具备认知能力的行为模仿机器人系统

doi:10.3969/j.issn.0255-8297.2022.01.002

应用科学学报 ›› 2022, Vol. 40 ›› Issue (1): 13-24.doi: 10.3969/j.issn.0255-8297.2022.01.002

具备认知能力的行为模仿机器人系统

包振山, 丁义龙, 张文博

北京工业大学信息学部, 北京 100124

收稿日期:2021-07-29 发布日期:2022-01-28
通信作者: 张文博,博士,研究方向为认知计算、异构计算、行为的语义理解。E-mail:zhangwenbo@bjut.edu.cn E-mail:zhangwenbo@bjut.edu.cn

Behavior Imitation Robotic System with Cognition Capacity

BAO Zhenshan, DING Yilong, ZHANG Wenbo

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Received:2021-07-29 Published:2022-01-28

摘要/Abstract

摘要： 行为模仿是现代机器人展现其智能化水平的重要技术之一，如何使机器人模仿出的行为动作与示教者相似且自然一直是该领域的热门研究课题。为此，基于朴素方法设计了一种改进的机器人行为模仿框架。该框架使用普通单目摄像头采集示教者动作，在朴素方法中引入行为语义识别模块与关键动作提取模块，使得机器人可以在理解示教者行为动作语义的基础上进行行为模仿。最后将该框架部署在HBE-ROBONOVA-AI Ⅱ人形机器人平台上，输入自主采集的单人动作视频数据进行实验。与目前其他主流框架的实验效果相比，该框架可以使得机器人在精确性、平衡性、相似性三方面的综合表现更加优秀，同时展现了机器人对于示教者行为动作独特的认知能力。

关键词: 行为模仿, 人形机器人, 行为语义识别, 关键动作提取, 认知计算

Abstract: Behavioral imitation is one of the important technologies for robots to show their intelligence. How to make the behaviors and actions imitated by robots similar to the demonstrating actions of human has become a hot research topic. In this paper, we design an improved robot behavior modeling framework based on simple method. The framework collects teaching action using normal monocular camera, and introduces behavior semantic recognition module and key action extraction module into the simple method. The framework enables robots to understand instructor's behavior semantics and then imitate instructor's behaviors. Finally, this framework is deployed on the HBE-ROBONOVA-AI II humanoid robot platform, and experiments are conducted using independently collected single-person action video data as input. Compared with the experimental results of other mainstream frameworks, this framework works with more excellent comprehensive performance in three aspects of accuracy, balance and similarity, and demonstrates a unique cognitive ability to instructor's behaviors.

Key words: behavior imitation, humanoid robot, behavior semantic recognition, key action extraction, cognitive computing

中图分类号:

TP242.6

包振山, 丁义龙, 张文博. 具备认知能力的行为模仿机器人系统[J]. 应用科学学报, 2022, 40(1): 13-24.

BAO Zhenshan, DING Yilong, ZHANG Wenbo. Behavior Imitation Robotic System with Cognition Capacity[J]. Journal of Applied Sciences, 2022, 40(1): 13-24.

参考文献

[1] 杨锦隆, 施明辉, 晁飞, 等. 基于深度学习进行动作模仿的舞蹈机器人[J]. 厦门大学学报(自然科学版), 2019, 58(5):759-766. Yang J L, Shi M H, Chao F, et al. Dance robot based on deep learning for motion imitation[J]. Journal of Xiamen University (Natural Science), 2019, 58(5):759-766. (in Chinese)
[2] Nakazawa A, Nakaoka S, Ikeuchi K, et al. Imitating human dance motions through motion structure analysis[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002:2539-2544
[3] Nakaoka S, Nakazawa A, Kanehiro F, et al. Learning from observation paradigm:leg task models for enabling a biped humanoid robot to imitate human dances[J]. The International Journal of Robotics Research, 2007, 26(8):829-844.
[4] Lin H, Chou C C. Humanoid robot motion imitation using Kinect[C]//IEEE International Conference on Advance Robotics and Intelligent System, 2015:1-4.
[5] Koenemann J, Bennewitz M. Whole-body imitation of human motions with a Nao humanoid[C]//20127th ACM/IEEE International Conference on Human-Robot Interaction, 2012:425-425.
[6] Ivaldi S, Babic J, Mistry M, et al. Special issue on whole-body control of contacts and dynamics for humanoid robots[J]. Autonomous Robots, 2016:425-428.
[7] Penco L, Clement B, Modugno V, et al. Robust real-time whole-body motion retargeting from human to humanoid[C]//IEEE-RAS 18th International Conference on Humanoid Robots, 2018:425-432.
[8] Jha A, Chiddarwar S S, Andulkar M V. An integrated approach for robot training using Kinect and human arm kinematics[C]//International Conference on Advances in Computing, Communications and Informatics, 2015:216-221.
[9] Cao Z, Simon T, Wei S E, et al. Realtime multi-person 2D pose estimation using part affinity fields[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017:7291-7299.
[10] Martinez J, Hossain R, Romero J, et al. A simple yet effective baseline for 3D human pose estimation[C]//IEEE International Conference on Computer Vision, 2017:2659-2668.
[11] Ko W R, Lee J, Jang M, et al. End-to-end learning of social behaviors for humanoid robots[C]//IEEE International Conference on Systems, Man, and Cybernetics, 2020:1200-1205.
[12] Mansard N, Stasse O, Evrard P, et al. A versatile generalized inverted kinematics implementation for collaborative working humanoid robots:the stack of tasks[C]//IEEE International Conference on Advanced Robotics, 2009:1-6.
[13] Suleiman W, Yoshida E, Kanehiro F, et al. On human motion imitation by humanoid robot[C]//IEEE International Conference on Robotics and Automation, 2008:2697-2704.
[14] Koenemann J, Burget F, Bennewitz M. Real-time imitation of human whole-body motions by humanoids[C]//IEEE International Conference on Robotics and Automation, 2014:2806-2812.

具备认知能力的行为模仿机器人系统

Behavior Imitation Robotic System with Cognition Capacity

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	季蕾, 樊春霞. 基于随机时延的多无人机编队控制方法[J]. 应用科学学报, 2019, 37(4): 551-564.
[2]	郭立新1，赵雷平2. 无线传感器网络中的多种追逃策略[J]. 应用科学学报, 2009, 27(3): 231-237.
[3]	段华, 赵东标. 一种新的移动机器人动态避障方法[J]. 应用科学学报, 2006, 24(5): 525-528.
[4]	谈士力, 张海洪, 龚振邦. 爬壁机器人移动机构越障功能研究[J]. 应用科学学报, 2001, 19(1): 40-43.