Incorporating Safety Into Parametric Dynamic Movement Primitives

被引:6
|
作者
Kim, Hyoin [1 ,2 ]
Seo, Hoseong [1 ,2 ]
Choi, Seungwon [1 ,2 ]
Tomlin, Claire J. [3 ]
Kim, H. Jin [1 ,2 ]
机构
[1] Seoul Natl Univ, Mech & Aerosp Engn Dept, Seoul 151744, South Korea
[2] Seoul Natl Univ, Automat & Syst Res Inst, Seoul 151744, South Korea
[3] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
关键词
Learning from demonstration; motion and path planning; manipulation planning;
D O I
10.1109/LRA.2019.2900762
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Parametric dynamic movement primitives (PDMPs) are powerful motion representation algorithms, which encode multiple demonstrations and generalize them. As an online trajectory from PDMPs emulates the provided demonstrations, managing the safety guarantee of the demonstrations for a given scenario is an important issue. This letter presents a process to manage the demonstration set in PDMPs when some demonstrations are poor in terms of safety. Our proposed process distinguishes safe motion primitives from unsafe ones. In order to establish a criterion for determining whether a motion is safe or not, we calculate the safe region of the PDMPs parameters called style parameters using an optimization technique. In the optimization formulation, we calculate the unsafe style parameters that produce the closest motion to the unsafe region of the state space. By eliminating unsafe demonstrations with the parameters based on the safety criterion, and replacing them with new safe ones, we incorporate safety in the PDMPs framework. Simulation and experimental results validate that the proposed process can expand the motion primitives in the PDMPs framework to the new environmental settings by efficiently utilizing the previous demonstrations.
引用
收藏
页码:2260 / 2267
页数:8
相关论文
共 50 条
  • [41] Impact of Body Parameters on Dynamic Movement Primitives for Robot Control
    Kuppuswamy, Naveen
    Alessandro, Cristiano
    PROCEEDINGS OF THE 2ND EUROPEAN FUTURE TECHNOLOGIES CONFERENCE AND EXHIBITION 2011 (FET 11), 2011, 7 : 166 - 168
  • [42] Learning from demonstration using improved dynamic movement primitives
    Wang, Tiantian
    Yan, Liang
    Wang, Gang
    Gao, Xiaoshan
    Du, Nannan
    Chen, I-Ming
    PROCEEDINGS OF THE 2021 IEEE 16TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2021), 2021, : 2130 - 2135
  • [43] Mobile Robot Path Planning Based on Dynamic Movement Primitives
    Jiang, Minghao
    Chen, Yang
    Zheng, Wenlei
    Wu, Huaiyu
    Cheng, Lei
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 980 - 985
  • [44] Learning Underwater Intervention Skills Based on Dynamic Movement Primitives
    Yang, Xuejiao
    Zhang, Yunxiu
    Li, Rongrong
    Zheng, Xinhui
    Zhang, Qifeng
    ELECTRONICS, 2024, 13 (19)
  • [45] Learning Stylistic Dynamic Movement Primitives from Multiple Demonstrations
    Matsubara, Takamitsu
    Hyon, Sang-Ho
    Morimoto, Jun
    IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, : 1277 - 1283
  • [46] Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields
    Chi, Mingshan
    Yao, Yufeng
    Liu, Yaxin
    Zhong, Ming
    APPLIED SCIENCES-BASEL, 2019, 9 (08):
  • [47] Constrained Dynamic Movement Primitives for Collision Avoidance in Novel Environments
    Shaw, Seiji
    Jha, Devesh K.
    Raghunathan, Arvind U.
    Corcodel, Radu
    Romeres, Diego
    Konidaris, George
    Nikovski, Daniel
    2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, IROS, 2023, : 3672 - 3679
  • [48] Training of deep neural networks for the generation of dynamic movement primitives
    Pahic, Rok
    Ridge, Barry
    Gams, Andrej
    Morimoto, Jun
    Ude, Ales
    NEURAL NETWORKS, 2020, 127 : 121 - 131
  • [49] A Framework for Learning Dynamic Movement Primitives with Deep Reinforcement Learning
    Noohian, Amirhossein
    Raisi, Mehran
    Khodaygan, Saeed
    2022 10TH RSI INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM), 2022, : 329 - 334
  • [50] From RGB images to Dynamic Movement Primitives for planar tasks
    Sidiropoulos, Antonis
    Doulgeri, Zoe
    2023 IEEE-RAS 22ND INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS, HUMANOIDS, 2023,