Shared autonomy (SA), which combines user inputs with autonomous capabilities, presents a significant opportunity for assistive robotics. A key challenge in SA is the dimensionality gap: the mismatch between low-dimensional user inputs from familiar interfaces (e.g., 2D joysticks) and the high-dimensional control required by robot manipulators. To enhance usability and acceptance, this mapping must be as simple and intuitive as possible. We introduce GeoSACS, a geometric framework for SA. GeoSACS uses canal surfaces to encode task structure with as few as two demonstrations. While the robot moves autonomously along the canal, users can then make corrections on the 2D planar circular cross-sections orthogonal to the robot motion. By leveraging geometric structure to partition the 6D control space between the robot and the user, GeoSACS allows the intuitive mapping of 2D user inputs to 6D end-effector control. We describe GeoSACS and evaluate its underlying assumptions in a user study against two baselines. Results from the study demonstrate reduced workload and improved performance, providing insights for the design of future SA systems.

Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or adapt existing ones to new situations. Recent advances in AI, including learning approaches like reinforcement learning and architectures like transformers and foundation models, combined with access to massive datasets, have created attractive opportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected data, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized area of work we term Human-Interactive Robot Learning (HIRL). This paradigm, wherein robots and humans interact during the learning process , is at the intersection of multiple fields (AI, robotics, human–computer interaction, design and others) and holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through interaction), align with human preferences (as humans can guide the robot behavior toward their expectations), and explore more meaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result in robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this article is to provide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and current open or underexplored challenges related to four themes—namely, human, robot learning, interaction, and broader context. The article includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to broad recommendations and a call for action for the growing HIRL community.

While shared autonomy offers significant potential for assistive robotics, key questions remain about how to effectively map 2D control inputs to 6D robot motions. An intuitive framework should allow users to input commands effortlessly, with the robot responding as expected, without users needing to anticipate the impact of their inputs. In this article, we propose a dynamic input mapping framework that links joystick movements to motions on control frames defined along a trajectory encoded with canal surfaces. We evaluate our method in a user study with 20 participants, demonstrating that our input mapping framework reduces the workload and improves usability compared to a baseline mapping with similar motion encoding. To prepare for deployment in assistive scenarios, we built on the development from the accessible gaming community to select an accessible control interface. We then tested the system in an exploratory study, where three wheelchair users controlled the robot for both daily living activities and a creative painting task, demonstrating its feasibility for users closer to our target population.

There are a multitude of robotic application domains that touch on the field of human-robot interaction (HRI). From modern manufacturing involving human-robot teams, to personal care robots assisting the elderly, the roles that robots are being tasked with and the nature of interactions with humans are constantly shifting. Even the nature of interaction has changed to incorporate wearable technologies such as exoskeletons to enhance human capabilities, and advanced prosthetics to restore those abilities that have been lost. With this ever-evolving spectrum of HRI, the capacity of measurement science to evaluate, assess, and assure performance and safety struggles to keep up. Building on our previous five-workshop series on Test Methods and Metrics for Effective HRI, NIST presents a new series on evaluative methodologies for accelerating the pipeline from cutting-edge HRI research to state-of-practice. This workshop will address issues regarding 1) data collection and reporting for replicability and system validation, 2) test design and execution for performance verification, and 3) cross-modality artifact design for real-world application-adjacent technology transfer. The goal of this workshop is to accelerate and accommodate accessibility to HRI research results, and address the specific key performance indicators that would establish end-user trust and acceptance of emerging HRI technologies.

Many industrial tasks-such as sanding, installing fasteners, and wire harnessing-are difficult to automate due to task complexity and variability. We instead investigate deploying robots in an assistive role for these tasks, where the robot assumes the physical task burden and the skilled worker provides both the high-level task planning and low-level feedback necessary to effectively complete the task. In this article, we describe the development of a system for flexible human-robot teaming that combines state-of-the-art methods in end-user programming and shared autonomy and its implementation in sanding applications. We demonstrate the use of the system in two types of sanding tasks, situated in aircraft manufacturing, that highlight two potential workflows within the human-robot teaming setup. We conclude by discussing challenges and opportunities in human-robot teaming identified during the development, application, and demonstration of our system.

We present a participatory design method to design human-robot interactions with older adults and its application through a case study of designing an assistive robot for a senior living facility. The method, called Situated Participatory Design (sPD), was designed considering the challenges of working with older adults and involves three phases that enable designing and testing use scenarios through realistic, iterative interactions with the robot. In design sessions with nine residents and three caregivers, we uncovered a number of insights about sPD that help us understand its benefits and limitations. For example, we observed how designs evolved through iterative interactions and how early exposure to the robot helped participants consider using the robot in their daily life. With sPD, we aim to help future researchers to increase and deepen the participation of older adults in designing assistive technologies.

We investigate how robotic camera systems can offer new capabilities to computer-supported cooperative work through the design, development, and evaluation of a prototype system called Periscope. With Periscope, a local worker completes manipulation tasks with guidance from a remote helper who observes the workspace through a camera mounted on a semi-autonomous robotic arm that is co-located with the worker. Our key insight is that the helper, the worker, and the robot should all share responsibility of the camera view-an approach we call shared camera control. Using this approach, we present a set of modes that distribute the control of the camera between the human collaborators and the autonomous robot depending on task needs. We demonstrate the system's utility and the promise of shared camera control through a preliminary study where 12 dyads collaboratively worked on assembly tasks. Finally, we discuss design and research implications of our work for future robotic camera systems that facilitate remote collaboration.

In telemanipulation, showing the user multiple views of the remote environment can offer many benefits, although such different views can also create a problem for control. Systems must either choose a single fixed control frame, aligned with at most one of the views or switch between view-aligned control frames, enabling view-aligned control at the expense of switching costs. In this paper, we explore the trade-off between these options. We study the feasibility, benefits, and drawbacks of switching the user's control frame to align with the actively used view during telemanipulation. We additionally explore the effectiveness of explicit and implicit methods for switching control frames. Our results show that switching between multiple view-specific control frames offers significant performance gains compared to a fixed control frame. We also find personal preferences for explicit or implicit switching based on how participants planned their movements. Our findings offer concrete design guidelines for future multi-camera interfaces.

The Participatory Design and End-User Program-ming for Human-Robot Interaction (HRI) workshop aims to advance research on how to design systems that can be used by end users to program robots. There tends to be a fracture in HRI between the technical designers of robot programs (often engineers or computer scientists) and the actual users of such robots. Developers have the capabilities to program robots but often lack insights possessed by domain experts, sometimes leading to technically interesting but impractical systems. With this workshop, we aim to bridge two different methods often used individually within the wider HRI community to involve end users in robot program design: Participatory Design (PD) and End-User Programming (EUP). Both methods empower end users to co-produce robots addressing real-world needs. However, there have been limited opportunities to unite researchers who specialize in these areas and engage in mutual learning. We will address this shortcoming with a full-day workshop, which will put the PD and EUP communities in touch, inviting speakers from both sides and welcoming a wide range of publications from describing new end-user programming methods to compiling insights learned from conducting participatory design studies.

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using nonlinear fitting and the iterative closest point algorithm. When the fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure for one degree-of-freedom (DOF) objects with revolute joints and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that integrates our method into an end-to-end system for articulating a remote valve.

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.

We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to “retask” robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot’s perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications.

Interacting physically with robots and sharing environment with them leads to situations where humans and robots have to cross each other in narrow corridors. In these cases, the robot has to make space for the human to pass. From observation of human-human crossing behaviours, we isolated two main factors in this avoiding behaviour: body rotation and sliding motion. We implemented a robot controller able to vary these factors and explored how this variation impacted on people's perception. Results from a within-participants study involving 23 participants show that people prefer a robot rotating its body when crossing them. Additionally, a sliding motion is rated as being warmer. These results show the importance of social avoidance when interacting with humans.

This paper presents a longitudinal case study of Robot Assisted Therapy for cardiac rehabilitation. The patient, who is a 60-year old male that suffered a myocardial infarction and received angioplasty surgery, successfully recovered after 35 sessions of rehabilitation with a social robot, lasting 18 weeks. The sessions took place directly at the clinic and relied on an exercise regime which was designed by the clinicians and delivered with the support of a social robot and a sensor suite. The robot monitored the patient's progress, and provided personalised encouragement and feedback. We discuss the recovery of the patient and illustrate how the use of a social robot, its sensory systems and its personalised interaction was instrumental to maintain engagement with the programme and to the patient's recovery. Of note is a critical event that was promptly detected by the robot, which allowed fast intervention measures to be taken by the medical staff for the referral of the patient for further surgery.

The Social Robots in Therapy workshop series aims at advancing research topics related to the use of robots in the contexts of Social Care and Robot-Assisted Therapy (RAT). Robots in social care and therapy have been a long time promise in HRI as they have the opportunity to improve patients life significantly. Multiple challenges have to be addressed for this, such as building platforms that work in proximity with patients, therapists and health-care professionals; understanding user needs; developing adaptive and autonomous robot interactions; and addressing ethical questions regarding the use of robots with a vulnerable population. The full-day workshop follows last year's edition which centered on how social robots can improve health-care interventions, how increasing the degree of autonomy of the robots might affect therapies, and how to overcome the ethical challenges inherent to the use of robot assisted technologies. This 2ndedition of the workshop will be focused on the importance of equipping social robots with socio-emotional intelligence and the ability to perform meaningful and personalized interactions. This workshop aims to bring together researchers and industry experts in the fields of Human-Robot Interaction, Machine Learning and Robots in Health and Social Care. It will be an opportunity for all to share and discuss ideas, strategies and findings to guide the design and development of robot-assisted systems for therapy and social care implementations that can provide personalize, natural, engaging and autonomous interactions with patients (and health-care providers).

We present in this paper our work towards a new dynamic method of generating spatial referring expressions. While people are generally ambiguous in their description of locations, previous methods of artificial generation mostly considered non-ambiguous descriptions. However, to increase the naturalness of interaction and share workload in the communication, robots should be able to generate language in a more dynamic way. Our method initially produces ambiguous spatial referring expressions followed by dynamically generating repair statements. We built a classifier using data from 18 participants as they described locations to each other. We perform a preliminary analysis on this method using two further pilot studies.

An increasing number of human-robot interaction (HRI) studies are now taking place in applied settings with children. These interactions often hinge on verbal interaction to effectively achieve their goals. Great advances have been made in adult speech recognition and it is often assumed that these advances will carry over to the HRI domain and to interactions with children. In this paper, we evaluate a number of automatic speech recognition (ASR) engines under a variety of conditions, inspired by real-world social HRI conditions. Using the data collected we demonstrate that there is still much work to be done in ASR for child speech, with interactions relying solely on this modality still out of reach. However, we also make recommendations for child-robot interaction design in order to maximise the capability that does currently exist.

Human-Robot Interaction (HRI) research requires the integration and cooperation of multiple disciplines, technical and social, in order to make progress. In many cases using different motivations, each of these disciplines bring with them different assumptions and methodologies. We assess recent trends in the field of HRI by examining publications in the HRI conference over the past three years (over 100 full papers), and characterise them according to 14 categories. We focus primarily on aspects of methodology. From this, a series of practical recommendations based on rigorous guidelines from other research fields that have not yet become common practice in HRI are proposed. Furthermore, we explore the primary implications of the observed recent trends for the field more generally, in terms of both methodology and research directions. We propose that the interdisciplinary nature of HRI must be maintained, but that a common methodological approach provides a much needed frame of reference to facilitate rigorous future progress.

An increasing amount of research is being conducted to determine how a robot tutor should behave socially in educational interactions with children. Both human-human and human-robot interaction literature predicts an increase in learning with increased social availability of a tutor, where social availability has verbal and nonverbal components. Prior work has shown that greater availability in the nonverbal behaviour of a robot tutor has a positive impact on child learning. This paper presents a study with 67 children to explore how social aspects of a tutor robot's speech influences their perception of the robot and their language learning in an interaction. Children perceive the difference in social behaviour between `low' and `high' verbal availability conditions, and improve significantly between a pre- and a post-test in both conditions. A longer-term retention test taken the following week showed that the children had retained almost all of the information they had learnt. However, learning was not affected by which of the robot behaviours they had been exposed to. It is suggested that in this short-term interaction context, additional effort in developing social aspects of a robot's verbal behaviour may not return the desired positive impact on learning gains.

The field of Human-Robot Interaction (HRI) is increasingly exploring the use of social robots for educating children. Commonly, non-academic audiences will ask how robots compare to humans in terms of learning outcomes. This question is also interesting for social roboticists as humans are often assumed to be an upper benchmark for social behaviour, which influences learning. This paper presents a study in which learning gains of children are compared when taught the same mathematics material by a robot tutor and a non-expert human tutor. Significant learning occurs in both conditions, but the children improve more with the human tutor. This difference is not statistically significant, but the effect sizes fall in line with findings from other literature showing that humans outperform technology for tutoring. We discuss these findings in the context of applying social robots in child education.

Subjective appreciation and performance evaluation of a robot by users are two important dimensions for Human-Robot Interaction, especially as increasing numbers of people become involved with robots. As roboticists we have to carefully design robots to make the interaction as smooth and enjoyable as possible for the users, while maintaining good performance in the task assigned to the robot. In this paper, we examine the impact of providing a robot with learning capabilities on how users report the quality of the interaction in relation to objective performance. We show that humans tend to prefer interacting with a learning robot and will rate its capabilities higher even if the actual performance in the task was lower. We suggest that adding learning to a robot could reduce the apparent load felt by a user for a new task and improve the user's evaluation of the system, thus facilitating the integration of such robots into existing work flows.

The role of robot social behaviour in changing people's behaviour is an interesting and yet still open question, with the general assumption that social behaviour is beneficial. In this study, we examine the effect of socially contingent robot behaviours on a charity collection task. Manipulating only behavioural cues (maintaining the same verbal content), we show that when the robot exhibits contingent behaviours consistent with those observable in humans, this results in a 32% increase in money collected over a non-reactive robot. These results suggest that apparent social agency on the part of the robot, even when subtle behavioural cues are used, can result in behavioural change on the part of the interacting human.

This paper presents the results of a study on the exploitation of compliance in structures made of self-reconfigurable modular robots - Roombots. This research was driven by the following three hypotheses: (1) compliance can improve locomotion performance; (2) different types of compliance will result in diverse locomotion behaviors; (3) control parameters optimized for a medium level of compliance will perform better for other values of compliance than parameters optimized for extremal compliance. Two types of in-series compliant elements were tested, with five different stiffness values for each of them, on a structure made of two Roombots modules. We ran dedicated on-line locomotion parameter optimizations for six different configurations and evaluated their performance for different stiffness values. Hypothesis 1 was confirmed for both types of compliant elements, with a peak of performance for an optimal level of compliance. The variety of locomotion strategies obtained for the different structures confirms hypothesis 2. Hypothesis 3 was only partially confirmed.