@article {IJRRLosey2021, title = {Physical interaction as communication: Learning robot objectives online from human corrections}, journal = {The International Journal of Robotics Research}, volume = {41}, number = {1}, year = {2022}, month = {Jan 2022}, pages = {02783649211050958}, chapter = {20-44}, abstract = {

When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human{\textendash}robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.

}, doi = {10.1177/02783649211050958}, url = {https://doi.org/10.1177/02783649211050958}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_IJRR2021.pdf}, author = {Dylan P. Losey and Andrea Bajcsy and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1862, title = {Learning from Physical Human Corrections, One Feature at a Time}, year = {2018}, month = {03/2018}, publisher = {ACM/IEEE}, address = {Chicago, USA}, abstract = {

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot{\textquoteright}s behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

}, doi = {10.1145/3171221.3171267}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_HRI2018.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1830, title = {Learning Robot Objectives from Physical Human Interaction}, year = {2017}, month = {11/2017}, pages = {217-226}, publisher = {PMLR}, address = {Mountain View, CA}, 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{\textquoteright}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.

}, keywords = {learning from demonstration, physical human-robot interaction}, url = {http://proceedings.mlr.press/v78/bajcsy17a.html}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CoRL_2017.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} }