ReviewPloS one2021
Review of machine learning methods in soft robotics.
Review in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Multi-modal sensing in colonoscopy: a data-driven approach.IEEE robotics and automation letters · 2026Article
- Noise enhances odor source localization.ArXiv · 2026Article
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- Nature-Inspired Innovation in Electrical Engineering Technologies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A unified framework for soft inflatable fabric actuators.Scientific reports · 2025Article
- Vat Photopolymerization of Smart Polymers for Biomedical Applications.ACS applied polymer materials · 2025Review
- Design and Machine Learning Modeling of a Multi-Degree-of-Freedom Bionic Pneumatic Soft Actuator.Biomimetics (Basel, Switzerland) · 2025Article
- A Novel Enhanced Methodology for Position and Orientation Control of the I-SUPPORT Robot.Biomimetics (Basel, Switzerland) · 2025Article
- Article
- A Bio-Inspired Data-Driven Locomotion Optimization Framework for Adaptive Soft Inchworm Robots.Biomimetics (Basel, Switzerland) · 2025Article
- Research Progress in Electroactive Polymers for Soft Robotics and Artificial Muscle Applications.Polymers · 2025Review
- Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder.Heliyon · 2025Article
- Data-driven modeling and identification of a bistable soft-robot element based on dielectric elastomer.Frontiers in robotics and AI · 2025Article
- Explainable machine learning model of disorganisation in swarms of drones.Scientific reports · 2024Article
- Advancing materials science through next-generation machine learning.Current opinion in solid state & materials science · 2024Article
- Chorda Dorsalis System as a Paragon for Soft Medical Robots to Design Echocardiography Probes with a New SOM-Based Steering Control.Biomimetics (Basel, Switzerland) · 2024Article
- Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications.Micromachines · 2024Review
- Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.National science review · 2024Review
- Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.Soft robotics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.
Indexed as
Identifiers
What 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.