Learning Robot Objectives from Physical Human Interaction

Andrea Bajcsy, Dylan P. Losey, Marcia K. O’Malley, Anca D. Dragan
; Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:217-226, 2017.

Abstract

When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot’s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.

Cite this Paper


BibTeX
@InProceedings{pmlr-v78-bajcsy17a, title = {Learning Robot Objectives from Physical Human Interaction}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O’Malley and Anca D. Dragan}, booktitle = {Proceedings of the 1st Annual Conference on Robot Learning}, pages = {217--226}, year = {2017}, editor = {Sergey Levine and Vincent Vanhoucke and Ken Goldberg}, volume = {78}, series = {Proceedings of Machine Learning Research}, month = {13--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v78/bajcsy17a/bajcsy17a.pdf}, url = {http://proceedings.mlr.press/v78/bajcsy17a.html}, abstract = {When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot’s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.} }
Endnote
%0 Conference Paper %T Learning Robot Objectives from Physical Human Interaction %A Andrea Bajcsy %A Dylan P. Losey %A Marcia K. O’Malley %A Anca D. Dragan %B Proceedings of the 1st Annual Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2017 %E Sergey Levine %E Vincent Vanhoucke %E Ken Goldberg %F pmlr-v78-bajcsy17a %I PMLR %J Proceedings of Machine Learning Research %P 217--226 %U http://proceedings.mlr.press %V 78 %W PMLR %X When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot’s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.
APA
Bajcsy, A., Losey, D.P., O’Malley, M.K. & Dragan, A.D.. (2017). Learning Robot Objectives from Physical Human Interaction. Proceedings of the 1st Annual Conference on Robot Learning, in PMLR 78:217-226

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