Personalized Instruction of Physical Skills with a Social Robot

2024

Human-in-the-loop robot learning allows a robot to learn tasks more efectively with the help of humans in the role of teacher. While there is a large body of work on algorithms that leverage human input for better robot learning, there has been little attention to understanding how humans teach robots. In this paper, we provide preliminary results on how users strategize the use of demonstrations and evaluative feedback under a budget, and how these choices are infuenced by demographic variables such as gender. We implemented a learning algorithm that allows a simulated robot arm to learn three reaching tasks with the help of a human. We collected interaction data for a total of 58 participants, which shows that participants demonstrate a tendency to provide evaluative feedback earlier in their interactions compared to demonstrations, and that gender may have an infuence on teaching strategy. This preliminary analysis lays the foundation for future research aimed at developing tuneable computational models of diferent human teachers.

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020

Skilled motor behavior is critical in many human daily life activities and professions. The design of robots that can effectively teach motor skills is an important challenge in the robotics field. In particular, it is important to understand whether the involvement in the training of a robot exhibiting social behaviors impacts on the learning and the experience of the human pupils. In this study, we addressed this question and we asked participants to learn a complex task-stabilizing an inverted pendulum-by training with physical assistance provided by a robotic manipulandum, the Wristbot. One group of participants performed the training only using the Wristbot, whereas for another group the same physical assistance was attributed to the humanoid robot iCub, who played the role of an expert trainer and exhibited also some social behaviors. The results obtained show that participants of both groups effectively acquired the skill by leveraging the physical assistance, as they significantly improved their stabilization performance even when the assistance was removed. Moreover, learning in a context of interaction with a humanoid robot assistant led subjects to increased motivation and more enjoyable training experience, without negative effects on attention and perceived effort. With the experimental approach presented in this study, it is possible to investigate the relative contribution of haptic and social signals in the context of motor learning mediated by human-robot interaction, with the aim of developing effective robot trainers.

Robotica

A gap of several decades exists between the latest technological achievements in humanoid robots, machine learning algorithms, and artificial intelligence, and the applications used by professional caregivers, especially in the field of pediatric rehabilitation. Robotics Agent Coacher for Cerebral Palsy motor Function (RAC CP Fun) is an attempt to implement advanced humanoid robotic technology, principles of Socially Assistive Robotics (SAR), and a motor learning approach in rehabilitation of movement disorders of central origin. Application of the recent advances in these areas offers an opportunity for a leap forward in rehabilitation practice. RAC CP Fun targets children with Cerebral Palsy (CP), and is designed to improve their motor function and activities of daily living. In terms of International Classification of Functioning, Disability, and Health (ICF) accepted by the World Health Organization, the novel algorithms of RAC CP Fun aim to facilitate children's activity and participation through pleasurable game-like human-robot interaction. At the same time, these algorithms utilize the motor learning approach, which is based on principles of repetition, stages of learning, appropriate feedback, random practice, and enriched environments. In this paper we introduce the experimental architecture, scenarios, and architectural design of RAC CP Fun, and findings from pilot experiments. Implementation of socially assistive robotics in neuromuscular rehabilitation may also stimulate the development of new treatment interventions for a wide variety of diseases and disorders through effective physiotherapy practice.

arXiv (Cornell University), 2021

In this paper, we investigate the roles that social robots can take in physical exercise with human partners. In related work, robots or virtual intelligent agents take the role of a coach or instructor whereas in other approaches they are used as motivational aids. These are two "paradigms", so to speak, within the small but growing area of robots for social exercise. We designed an online questionnaire to test whether the preferred role in which people want to see robots would be the companion or the coach. The questionnaire asks people to imagine working out with a robot with the help of three utilized questionnaires: (1) CART-Q which is used for judging coach-athlete relationships, (2) the mind perception questionnaire and (3) the System Usability Scale (SUS). We present the methodology, some preliminary results as well as our intended future work on personal robots for coaching. CCS CONCEPTS • Computing methodologies → Theory of mind; Cognitive science; • Human-centered computing → User studies;

international conference on user modeling adaptation and personalization, 2011

Our goal is to develop methods for non-experts to teach complex behaviors to autonomous agents (such as robots) by accommodating "natural" forms of human teaching. We built a prototype interface allowing humans to teach a simulated robot a complex task using several techniques and report the results of 44 human participants using this interface. We found that teaching styles varied considerably but can be roughly categorized based on the types of interaction, patterns of testing, and general organization of the lessons given by the teacher. Our study contributes to a better understanding of human teaching patterns and makes specific recommendations for future human-robot interaction systems.

Human-Centered Software Engineering, 2020

The number of physiotherapists and occupational therapists is not sufficient to cope with the demands of the increasing number of stroke survivors worldwide. These patients need specific training to promote recovery and prevent stroke-related disability. The paper discusses aspects how a social humanoid robots might serve as therapeutic assistant. It is not intended to replace human therapists, but to provide them with therapeutic assistance once therapeutic decisions are taken and the therapy has been introduced to the patient by the human therapists and the day to day practice needs to be supervised. The paper provides a case study of an HCD approach and exemplifies the strategy of digitalizing training tasks, the identification of exceptions, and ways of modelling the dynamic behaviour of the humanoid robot. This is demonstrated by task models. Some extensions of task models are suggested.

One important challenge for roboticists in the coming years will be to design robots to teach humans new skills or to lead humans in activities which require sustained motivation (e.g. physiotherapy, skills training). In the current study, we tested the hypothesis that if a robot teacher invests physical effort in adapting to a human learner in a context in which the robot is teaching the human a new skill, this would facilitate the human's learning. We also hypothesized that the robot teacher's effortful adaptation would lead the human learner to experience greater rapport in the interaction. To this end, we devised a scenario in which the iCub and a human participant alternated in teaching each other new skills. In the high effort condition, the iCub slowed down his movements when repeating a demonstration for the human learner, whereas in the low effort condition he sped the movements up when repeating the demonstration. The results indicate that participants indeed learned more effectively when the iCub adapted its demonstrations, and that the iCub's apparently effortful adaptation led participants to experience him as more helpful.

2009

This paper presents a framework for parallel tracking of human hands and faces in real time, and is a partial solution to a larger project on human-robot interaction which aims at training autistic children using a humanoid robot in a realistic non-restricted environment. In addition to the framework, the results of tracking different hand waving patterns are shown. These patterns provide an easy to understand profile of hand waving, and can serve as the input for a classification algorithm.

Due to their quantitative nature, robotic systems are useful tools for systematically augmenting human behavior and performance in dynamic environments, such as therapeutic rehabilitation settings. The e cacy of human-robot interaction (HRI) in these settings will depend on the robot' s coaching style. Our goal was to investigate the influence of robot coaching styles designed to enhance motivation and encouragement on post-stroke individuals during motor task practice. We hypothesized that coaching styles incorporating user performance and preference would be preferred in a therapeutic HRI setting. We designed an evaluation study with seven individuals post stroke. A socially assistive robotics (SAR) system using three di erent coaching styles guided participants during performance of an upper extremity practice task. User preference was not significantly a ected by the di erent robot coaching styles in our participant sample (H(2) = 2.638, p = 0.267). However, trends indicated di erences in preference for the coaching styles. Our results provide insights into the design and use of SAR systems in therapeutic interactions aiming to influence user behavior.

Robotics and Autonomous Systems, 2014

We explore the potential for humanoid robots to interact with children in a dance activity. In this context, the robot plays the role of an instructor to guide the child through several dance moves to learn a dance phrase. We participated in 30 dance sessions in schools to study human-human interaction between children and a human dance teacher, and to identify the applied methodologies. Based on the strategies observed, both social and task-dependent, we implemented a robotic system capable of autonomously instructing dance sequences to children while displaying basic social cues to engage the child in the task. Experiments were performed in a hospital with the Nao robot interacting with 12 children through multiple encounters, when possible (18 sessions, 236 minutes). Observational analysis through video recordings and survey evaluations were used to assess the quality of interaction. Moreover, we introduce an involvement measure based on the aggregation of observed behavioral cues to to assess the level of interest in the interaction through time. The analysis revealed high levels of involvement, while highlighting the need for further research into social engagement and adaptation with robots over repeated sessions.