Generalizing Topological Task Graphs From Multiple Symbolic Demonstrations in Programming by Demonstration (PbD) Processes

被引:0
|
作者
Abbas, Tanveer [1 ]
MacDonald, Bruce A. [1 ]
机构
[1] Univ Auckland, Auckland 1, New Zealand
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Many programming by demonstration methods encode demonstrations into sequences of predefined symbols and then build a generalized task structure such as a topological graph. The longest common subsequence (LCS) algorithm is one of the potential techniques to help build generalized task structures from multiple sequences. However the LCS problem is NP hard, so a couple of suboptimal LCS approaches have been adopted in the past, involving a pair-wise comparison of sequences or a search for the common symbols within a small window. This paper argues that an LCS of multiple sequences results in a better generalization than pairwise comparison, and in many practical situations it is feasible to find an LCS of multiple sequences. So a novel LCS finding algorithm is presented for applications in the programming by demonstration domain. The algorithm has been extensively tested for sequences of random symbols and its application in a path planning example is presented.
引用
收藏
页数:6
相关论文
共 6 条
  • [1] Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration
    Huang, De-An
    Nair, Suraj
    Xu, Danfei
    Zhu, Yuke
    Garg, Animesh
    Li Fei-Fei
    Savarese, Silvio
    Niebles, Juan Carlos
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 8557 - 8566
  • [2] Learning Performance Graphs From Demonstrations via Task-Based Evaluations
    Puranic, Aniruddh G.
    Deshmukh, Jyotirmoy V.
    Nikolaidis, Stefanos
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (01) : 336 - 343
  • [3] Super Intendo: Semantic Robot Programming from Multiple Demonstrations for taskable robots
    French, Kevin David
    Kim, Ji Hwang
    Du, Yidong
    Goeddel, Elizabeth Mamantov
    Zeng, Zhen
    Jenkins, Odest Chadwicke
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2023, 166
  • [4] Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations
    Rana, M. Asif
    Li, Anqi
    Ravichandar, Harish
    Mukadam, Mustafa
    Chernova, Sonia
    Fox, Dieter
    Boots, Byron
    Ratliff, Nathan
    CONFERENCE ON ROBOT LEARNING, VOL 100, 2019, 100
  • [5] Task Dependent Trajectory Learning from Multiple Demonstrations Using Movement Primitives
    Vidakovic, Josip
    Jerbic, Bojan
    Sekoranja, Bojan
    Svaco, Marko
    Suligoj, Filip
    ADVANCES IN SERVICE AND INDUSTRIAL ROBOTICS, 2020, 980 : 275 - 282
  • [6] Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression
    Song, Zhilong
    Wang, Xiao
    Liu, Fangting
    Zhou, Qionghua
    Yin, Wan-Jian
    Wu, Hao
    Deng, Weiqiao
    Wang, Jinlan
    MATERIALS HORIZONS, 2023, 10 (05) : 1651 - 1660