Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges

Pablo G. Esteban; Daniel Hernández García; Hee Rin Lee; Pauline Chevalier; Paul Baxter; Cindy L. Bethel; Jainendra Shukla; Joan Oliver; Domènec Puig; Jason R. Wilson; Linda Tickle-Degnen; Madeleine Bartlett; Tony Belpaeme; Serge Thill; Kim Baraka; Francisco S. Melo; Manuela Veloso; David Becerra; Maja Matarić; Eduard Fosch-Villaronga; Jordi Albo-Canals; Gloria Beraldo; Emanuele Menegatti; Valentina De Tommasi; Roberto Mancin; Franca Benini; Zachary Henkel; Kenna Baugus; David C. May; Lucile Dupuy; Wendy A. Rogers; Ronit Feingold Polak; Shelly Levy-Tzedek; Dagoberto Cruz-Sandoval; Jesus Favela; Michelle J. Johnson; Mayumi Mohan; Rochelle Mendonca

arXiv · 1812.07613

Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges

View original source ↗Read source document ↗Export metadata ↓

Abstract

Robot-Assisted Therapy (RAT) has successfully been used in HRI research by including social robots in health-care interventions by virtue of their ability to engage human users both social and emotional dimensions. Research projects on this topic exist all over the globe in the USA, Europe, and Asia. All of these projects have the overall ambitious goal to increase the well-being of a vulnerable population. Typical work in RAT is performed using remote controlled robots; a technique called Wizard-of-Oz (WoZ). The robot is usually controlled, unbeknownst to the patient, by a human operator. However, WoZ has been demonstrated to not be a sustainable technique in the long-term. Providing the robots with autonomy (while remaining under the supervision of the therapist) has the potential to lighten the therapists burden, not only in the therapeutic session itself but also in longer-term diagnostic tasks. Therefore, there is a need for exploring several degrees of autonomy in social robots used in therapy. Increasing the autonomy of robots might also bring about a new set of challenges. In particular, there will be a need to answer new ethical questions regarding the use of robots with a vulnerable population, as well as a need to ensure ethically-compliant robot behaviours. Therefore, in this workshop we want to gather findings and explore which degree of autonomy might help to improve health-care interventions and how we can overcome the ethical challenges inherent to it.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Pablo G. Esteban, Daniel Hernández García, Hee Rin Lee, Pauline Chevalier, Paul Baxter, Cindy L. Bethel, Jainendra Shukla, Joan Oliver, Domènec Puig, Jason R. Wilson, Linda Tickle-Degnen, Madeleine Bartlett, Tony Belpaeme, Serge Thill, Kim Baraka, Francisco S. Melo, Manuela Veloso, David Becerra, Maja Matarić, Eduard Fosch-Villaronga, Jordi Albo-Canals, Gloria Beraldo, Emanuele Menegatti, Valentina De Tommasi, Roberto Mancin, Franca Benini, Zachary Henkel, Kenna Baugus, David C. May, Lucile Dupuy, Wendy A. Rogers, Ronit Feingold Polak, Shelly Levy-Tzedek, Dagoberto Cruz-Sandoval, Jesus Favela, Michelle J. Johnson, Mayumi Mohan, Rochelle Mendonca. 2018-12-18. Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges. https://arxiv.org/abs/1812.07613

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Robotic Tele-Operation for Upper Aerodigestive Tract Microsurgery: System Design and Validation

Upper aerodigestive tract (UADT) treatments frequently employ transoral laser microsurgery (TLM) for procedures such as the removal of tumors or polyps. In TLM, a laser beam is used to cut target tissue, while forceps are employed to grasp, manipulate, and stabilize tissue within the UADT. Although TLM systems may rely on different technologies and interfaces, forceps manipulation is still predominantly performed manually, introducing limitations in ergonomics, precision, and controllability. This paper proposes a novel robotic system for tissue manipulation in UADT procedures, based on a novel end-effector designed for forceps control. The system is integrated within a teleoperation framework that employs a robotic manipulator with a programmed remote center of motion (RCM), enabling precise and constrained instrument motion while improving surgeon ergonomics. The proposed approach is validated through two experimental studies and a dedicated usability evaluation, demonstrating its effectiveness and suitability for UADT surgical applications.

cs.RO↗

arXiv2026

HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving

End-to-end autonomous driving models increasingly benefit from large vision-language models for semantic understanding, yet safe and reliable planning under long-tail conditions remains challenging, particularly in mixed-traffic environments involving heterogeneous road users and rare safety-critical interactions. This paper proposes HERMES, a holistic risk-aware end-to-end multimodal driving framework that explicitly incorporates long-tail semantic knowledge into trajectory planning. HERMES employs a foundation-model-assisted annotation pipeline to construct structured Long-Tail Scene Context and Long-Tail Planning Context, capturing hazard-centric scene information, maneuver intent, and risk-aware planning guidance. A Tri-Modal Driving Module then integrates multi-view visual observations, historical ego-motion, and long-tail semantic instructions through intent- and risk-aware conditioning for trajectory generation. Extensive experiments on a large-scale real-world long-tail driving benchmark demonstrate consistent improvements over representative recent baselines in overall planning performance and across diverse safety-critical scenarios. Ablation studies further validate the effectiveness and complementary roles of the major components within HERMES.

cs.RO↗

arXiv2026

EgoPush: Egocentric Multi-Object Rearrangement for Mobile Robots via Constrained Teacher Observability

Humans rearrange objects in cluttered environments using egocentric perception, actively moving to keep task-relevant spatial cues in view. Mobile robots have not matched this: rearrangement is usually built on a global pose estimate or a map, which is exactly what a robot carrying one camera lacks, while pushing keeps changing the scene it would have to be built from. We present EgoPush, which pushes objects into anchor-relative formations from onboard RGB-D alone, with no global localization, external tracking, or map at deployment, and transfers zero-shot to a TurtleBot in controlled and visually cluttered scenes. What makes this learnable turns out to be a property of the teacher rather than of the student: three privileged teachers trained with identical rewards, architecture, and hyperparameters all exceed $98\%$ success, yet their distilled egocentric students reach $0\%$, $54.8\%$, and $87.3\%$, the only variable being the teacher's observation function. EgoPush therefore trains the teacher under egocentric observability constraints, restricting it to visibility-limited cues and revealing target references only when the anchor is centrally visible, so that its supervision is recoverable by a depth-based student distilled online. Making the teacher trainable in the first place needs two further pieces: a role-grouped object-centric interface shared by teacher and student, and stage-wise temporally decayed rewards for long-horizon credit assignment. Videos, the playable task, and code are available at https://ai4ce.github.io/EgoPush/.

cs.RO↗