Learning Force-Relevant Skills from Human Demonstration
Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learnin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5262859 |
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| _version_ | 1832563733462777856 |
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| author | Xiao Gao Jie Ling Xiaohui Xiao Miao Li |
| author_facet | Xiao Gao Jie Ling Xiaohui Xiao Miao Li |
| author_sort | Xiao Gao |
| collection | DOAJ |
| description | Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly. |
| format | Article |
| id | doaj-art-ca0e10805f4d47559b392a979671f1c9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ca0e10805f4d47559b392a979671f1c92025-02-03T01:12:48ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/52628595262859Learning Force-Relevant Skills from Human DemonstrationXiao Gao0Jie Ling1Xiaohui Xiao2Miao Li3School of Power and Mechanical Engineering, Wuhan University, ChinaSchool of Power and Mechanical Engineering, Wuhan University, ChinaSchool of Power and Mechanical Engineering, Wuhan University, ChinaSchool of Power and Mechanical Engineering, Wuhan University, ChinaMany human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.http://dx.doi.org/10.1155/2019/5262859 |
| spellingShingle | Xiao Gao Jie Ling Xiaohui Xiao Miao Li Learning Force-Relevant Skills from Human Demonstration Complexity |
| title | Learning Force-Relevant Skills from Human Demonstration |
| title_full | Learning Force-Relevant Skills from Human Demonstration |
| title_fullStr | Learning Force-Relevant Skills from Human Demonstration |
| title_full_unstemmed | Learning Force-Relevant Skills from Human Demonstration |
| title_short | Learning Force-Relevant Skills from Human Demonstration |
| title_sort | learning force relevant skills from human demonstration |
| url | http://dx.doi.org/10.1155/2019/5262859 |
| work_keys_str_mv | AT xiaogao learningforcerelevantskillsfromhumandemonstration AT jieling learningforcerelevantskillsfromhumandemonstration AT xiaohuixiao learningforcerelevantskillsfromhumandemonstration AT miaoli learningforcerelevantskillsfromhumandemonstration |