In an effort towards the democratization of Robotics, this article presents a novel End-User Development framework called Robot Interfaces From Zero Experience (RIZE). The framework provides a set of useful software tools for the creation of robot-oriented software architectures and programming interfaces, as well as the modeling and execution of robot behaviors, with a specific emphasis on social behaviors. Programming interfaces built on top of RIZE enable professionals with different backgrounds and interests to design, adapt, and scale-up robotics applications. As an example of a programming interface, we present Open RIZE, which exploits an End-User Programming paradigm combining blocks, tables, and forms-filling interfaces. Unlike previous approaches, robot behavioral code generated by Open RIZE is intrinsically modular, re-usable, scalable, neutral to the employed programming language, and platform-agnostic. In the article, we present the main design guidelines and features of Open RIZE. Additionally, we perform an initial usability evaluation of the Open RIZE interface in an online workshop. Preliminary results using the System Usability Scale with 10 novice end-users indicate that Open RIZE is easy-to-use and learn.

Background: Socially assistive robots (SARs) are robotic technology platforms equipped with sensing (eg, through audio or visual) and acting (eg, speech and movement) capabilities to interact socially with users. SARs are increasingly adopted in physiotherapy to aid patients in their rehabilitation journey by providing feedback, motivation, and encouragement. However, while many studies have explored SAR implementation in physiotherapy, research involving clinical populations remains scarce, and the overall state of SAR deployment is unclear. Objective: This scoping review aimed to explore the use of SARs in physiotherapy with clinical populations, how the effectiveness of these interventions has been evaluated, and identify limitations and new areas of application and future work. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Review) reporting guideline extension, comprehensive searches based on SARs and physiotherapy were conducted in various databases. Title and abstract screening were performed by 1 reviewer, with full-text screening conducted by 2 reviewers. Data extraction, synthesis, and analysis were completed by 1 reviewer. Data on SAR roles were categorized and synthesized using content analysis. Other descriptive texts were summarized to improve readability. Results: Our findings suggest that SARs are commonly used in rehabilitation clinics and hospital inpatient settings, primarily for neurological conditions. In these interventions, SARs typically serve roles, such as coaching, demonstration, monitoring, and peer support. Their effectiveness is generally evaluated through clinical outcomes, user performance, functional measures, and metrics assessing the robots’ acceptability, usability, and perception. Conclusions: This scoping review highlighted SARs’ potential to address challenges faced by human therapists due to the demands of time-extending coaching and monitoring and the limited availability of therapists. Future research should focus on addressing the limitations identified in this scoping review, including small sample sizes, technical issues in both the robot and intervention design, sufficient involvement of key stakeholders in the design and development of SAR-based interventions, and conducting more clinical trials to investigate SAR intervention effectiveness.

When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from nonexpert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot would also be able to infer a human's true objectives even when the human fails to properly demonstrate these objectives due to a lack of expertise. In this article, we propose to jointly infer the expertise level and the objective function of a human given observations of their (possibly) nonoptimal demonstrations. Two inference approaches are proposed. In the first approach, inference is done over a finite discrete set of possible objective functions and expertise levels. In the second approach, the robot optimizes over the space of all possible hypotheses and finds the objective function and the expertise level that best explain the observed human behavior. We demonstrate our proposed approaches both in simulation and with real user data.

In shared human-robot environments, effective navigation requires robots to adapt to various pedestrian behaviors encountered in the real world. Most existing deep reinforcement learning algorithms for human-aware robot navigation typically assume that pedestrians adhere to a single walking behavior during training, limiting their practicality/performance in scenarios where pedestrians exhibit various types of behavior. In this work, we propose to enhance the generalization capabilities of human-aware robot navigation by employing Domain Randomization (DR) techniques to train navigation policies on a diverse range of simulated pedestrian behaviors with the hope of better generalization to the real world. We evaluate the effectiveness of our method by comparing the generalization capabilities of a robot navigation policy trained with and without DR, both in simulations and through a real-user study, focusing on adaptability to different pedestrian behaviors, performance in novel environments, and users' perceived comfort, sociability and naturalness. Our findings reveal that the use of DR significantly enhances the robot's social compliance in both simulated and real-life contexts.