Rationale:In Switzerland, 1 769 000 people are living with disabilities, and might need frequent physical therapy to maintain functions. However, most patients can only access therapy at a low frequency (due to insurance or availability of therapists) which might be insufficient. We believe that robotic-assisted physical therapy could help address this gap by helping patients to exercise more frequently. Due to the variability between pathologies and individuals, these physical human-robot interactions can take different forms and should be set by a therapist. However, as pointed in Van der Loos et al. (2016), there are critical needs for interfaces to personalize these physical human-robot interaction that can be used by non-experts in robotics. Objectives:This project’s main objective is to push the science on physical human-robot interaction and develop user-centered tools that can be shared with the community to personalize physically assistive behaviors. More precisely, this project aims to answer the following question: “How can end users easily and intuitively specify complex physically interactive robot behaviors?”, using rehabilitation robotics as a use case.The aims of the project are: (1) Develop new flexible behavior encodings for kinesthetic interaction between robots and people; (2) Develop multi-modal interfaces to specify rich kinesthetic behaviors; (3) Validate these systems in user studies with clinicians and patients.Methods:This project approaches the challenge of end-user design of kinesthetic robot behavior through participatory design (PD), a development method centered around the users of the technology. We will collaborate with an application partner (Centre Neu’Rhône) to develop interaction paradigms (Thread 1), behavior encodings (Thread 2), and interfaces (Thread 3) to support greater personalization and usability of physical human-robot interaction. Finally, we will evaluate our system in multiple user studies and conclude the project with a summative study where patients will be able to interact with the robot with little supervision (Thread 4). The project will leverage previous work from the PI (PD research, end-user programming, assistive robotics, and shared autonomy) and partners at the institution (learning from demonstration and behavior encodings) to develop parameterizable motions that can be easily and intuitively specified by end users, such as physical therapists.Expected Results:With this work, we aim to develop and make available new tools for end users to create rich kinesthetic behaviors. Through our user studies, we will demonstrate the applicability of our approach to rehabilitation therapy, showing that collaborative robots can be flexible tools to safely simplify access to therapy. Finally, we will open-source our findings to help the community build upon the research made in this project. These contributions are significant for the community, as there is today little research on the specification of rich kinesthetic behaviors directly by end users in allied health domains. Most research in end-user specification of physical behaviors remains centered on industrial application and misses challenges specific to this domain.Impact:This project will impact the robotic community by developing new control algorithms and interfaces for safe and personalizable physical human-robot interaction. On top of disseminating knowledge though publications in scientific venues (conferences and journals), this project will provide an open source flexible software implementation for the wider community. However, the impact will also reach beyond roboticists and researchers. By taking a user-centered approach, this project will help form the next generation of multidisciplinary researchers. Finally, this project also aims to have societal impacts by making personalized physical therapy more accessible and communicating with the public through workshops and other outreach activities.