ReviewNature2024
On human-in-the-loop optimization of human-robot interaction.
Review in Nature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Virtual Reality-Enabled Physical AI Training for Supportive Nursing Robotics: Nurse-in-the-Loop, Site-Specific Conceptual Framework.JMIR nursing · 2026Article
- A Multimodal Therapist-Supervised Robotic Gait-Training Platform with Phase-Synchronized Infrared Thermal Biofeedback: A Concept-Level Technical and Translational Healthcare Framework.Healthcare (Basel, Switzerland) · 2026Article
- Light-tunable charge dynamics enable ultralow-power bidirectional lead-free perovskite synapses for intelligent vision.Science advances · 2026Article
- Robot-assisted Computer-Implant surgery : A comprehensive review of system architecture, clinical accuracy, and emerging technologies.Journal of robotic surgery · 2026Review
- Improving outdoor navigation for people with blindness using an AI-driven smartphone application and personalized audio guidance.Nature biomedical engineering · 2026Article
- Automated high-resolution CT analysis outperforms visual assessment in predicting interstitial lung disease progression in SSc.Rheumatology (Oxford, England) · 2026Article
- A Portable Soft Robotic Glove with Fully Functional Thumb Assistance for Complex Dexterous Fine Motor Skills.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Explainable generative 3D design driven by social media semantics.Nature communications · 2026Article
- Validation of Dynamic Bayesian Optimization for Human-in-the-Loop Optimization of Exoskeleton Control at User-Driven Walking Speed.bioRxiv : the preprint server for biology · 2026Article
- Synthetic Data-Driven Exoskeleton Control via Contralateral Gait Fusion for Variable-Speed Walking.Biomimetics (Basel, Switzerland) · 2026Article
- Validation of Dynamic Bayesian Optimization for a Non-Stationary Human-in-the-Loop Optimization Problem.IEEE robotics and automation letters · 2026Article
- Algorithmic Habituation: A Neurocognitive and Systems-Based Framework for Human-AI Co-Adaptation.Brain sciences · 2026Article
- Estimating and interpreting how humans prioritize multiple movement goals during walking.Journal of neuroengineering and rehabilitation · 2026Article
- Real-Time Estimation of User Adaptation During Hip Exosuit-Assisted Walking Using Wearable Inertial Measurement Unit Data and Long Short-Term Memory Modeling.Biomimetics (Basel, Switzerland) · 2026Article
- Bioinspired growable humanoid robot with bone-mimetic linkages for versatile mobility.Science advances · 2026Article
- Ubiquitous sensing of marine activities via the cooperation of autonomous underwater vehicles.Scientific reports · 2026Article
- Space Physiology and Technology: Adaptations, Countermeasures, and Opportunities for Wearable Systems.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Interaction-based rapid heuristic optimization of exoskeleton assistance during walking.Communications engineering · 2025Article
- Emerging Implantable Sensor Technologies at the Intersection of Engineering and Brain Science.Biosensors · 2025Review
- The interplay between haptic guidance and personality traits in robotic-assisted motor learning.Journal of neuroengineering and rehabilitation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human-robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human-robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.
Indexed as
Identifiers
39322732What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.