A User Study on Personalized Stiffness Control and Task Specificity in Physical Human–Robot Interaction
An ideal physical human–robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of su...
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| Vydáno v: | Frontiers in robotics and AI Ročník 4 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Frontiers Media S.A
24.11.2017
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| ISSN: | 2296-9144, 2296-9144 |
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| Abstract | An ideal physical human–robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI that is calibrated for the user’s force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user’s interaction force profiles confirms this finding. |
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| AbstractList | An ideal physical human–robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI that is calibrated for the user’s force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user’s interaction force profiles confirms this finding. |
| Author | Gopinathan, Sugeeth Ötting, Sonja K. Steil, Jochen J. |
| Author_xml | – sequence: 1 givenname: Sugeeth surname: Gopinathan fullname: Gopinathan, Sugeeth – sequence: 2 givenname: Sonja K. surname: Ötting fullname: Ötting, Sonja K. – sequence: 3 givenname: Jochen J. surname: Steil fullname: Steil, Jochen J. |
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| CitedBy_id | crossref_primary_10_1016_j_rcim_2021_102127 crossref_primary_10_3389_fnbot_2021_626074 crossref_primary_10_3390_s23177381 crossref_primary_10_3389_fnbot_2020_590241 crossref_primary_10_3389_fnbot_2023_1084000 crossref_primary_10_1002_aisy_202400333 crossref_primary_10_1016_j_procir_2018_02_010 crossref_primary_10_1177_02783649211053650 crossref_primary_10_1145_3368854 crossref_primary_10_3389_fnbot_2020_578834 |
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