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–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.
This content will become publicly available on June 28, 2025
Charting User Experience in Physical Human-Robot Interaction
Robots increasingly interact with humans through touch, where people are touching or being touched by robots. Yet, little is known about how such interactions shape a user’s experience. To inform future work in this area, we conduct a systematic review of 44 studies on physical human-robot interaction (pHRI). Our review examines the parameters of the touch (e.g., the role of touch, location), the experimental variations used by researchers, and the methods used to assess user experience. We identify five facets of user experience metrics from the questionnaire items and data recordings for pHRI studies. We highlight gaps and methodological issues in studying pHRI and compare user evaluation trends with the Human-Computer Interaction (HCI) literature. Based on the review, we propose a conceptual model of the pHRI experience. The model highlights the components of such touch experiences to guide the design and evaluation of physical interactions with robots and inform future user experience questionnaire development.
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- Award ID(s):
- 2301335
- NSF-PAR ID:
- 10539038
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- ACM Transactions on Human-Robot Interaction
- Volume:
- 13
- Issue:
- 2
- ISSN:
- 2573-9522
- Page Range / eLocation ID:
- 1 to 29
- Subject(s) / Keyword(s):
- physical human-robot interaction tactile human-robot interaction haptics user experience systematic review
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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