Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement
المؤلفون المشاركون
Hu, Siyao
Kuchenbecker, Katherine J.
المصدر
Applied Bionics and Biomechanics
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-25، 25ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-02
دولة النشر
مصر
عدد الصفحات
25
التخصصات الرئيسية
الملخص EN
Learning from demonstration (LfD) enables a robot to emulate natural human movement instead of merely executing preprogrammed behaviors.
This article presents a hierarchical LfD structure of task-parameterized models for object movement tasks, which are ubiquitous in everyday life and could benefit from robotic support.
Our approach uses the task-parameterized Gaussian mixture model (TP-GMM) algorithm to encode sets of demonstrations in separate models that each correspond to a different task situation.
The robot then maximizes its expected performance in a new situation by either selecting a good existing model or requesting new demonstrations.
Compared to a standard implementation that encodes all demonstrations together for all test situations, the proposed approach offers four advantages.
First, a simply defined distance function can be used to estimate test performance by calculating the similarity between a test situation and the existing models.
Second, the proposed approach can improve generalization, e.g., better satisfying the demonstrated task constraints and speeding up task execution.
Third, because the hierarchical structure encodes each demonstrated situation individually, a wider range of task situations can be modeled in the same framework without deteriorating performance.
Last, adding or removing demonstrations incurs low computational load, and thus, the robot’s skill library can be built incrementally.
We first instantiate the proposed approach in a simulated task to validate these advantages.
We then show that the advantages transfer to real hardware for a task where naive participants collaborated with a Willow Garage PR2 robot to move a handheld object.
For most tested scenarios, our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Siyao& Kuchenbecker, Katherine J.. 2019. Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement. Applied Bionics and Biomechanics،Vol. 2019, no. 2019, pp.1-25.
https://search.emarefa.net/detail/BIM-1114744
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Siyao& Kuchenbecker, Katherine J.. Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement. Applied Bionics and Biomechanics No. 2019 (2019), pp.1-25.
https://search.emarefa.net/detail/BIM-1114744
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Siyao& Kuchenbecker, Katherine J.. Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement. Applied Bionics and Biomechanics. 2019. Vol. 2019, no. 2019, pp.1-25.
https://search.emarefa.net/detail/BIM-1114744
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1114744
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر