ReviewFrontiers in robotics and AI2021
A Survey for Machine Learning-Based Control of Continuum Robots.
Review in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Multi-Segment Extendable Soft Manipulator Driven by a Pneumatic-Tendon Coupling Mechanism.Biomimetics (Basel, Switzerland) · 2025Article
- The evolution of surgical precision: addressing and advancing the hardware of robotic end-effectors.Surgical endoscopy · 2025Review
- Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions.Sensors (Basel, Switzerland) · 2025Article
- Learning-based control for tendon-driven continuum robotic arms.Frontiers in robotics and AI · 2025Article
- Precision at scale: Machine learning revolutionizing laparoscopic surgery.World journal of clinical oncology · 2024Article
- Model-Free Control of a Soft Pneumatic Segment.Biomimetics (Basel, Switzerland) · 2024Article
- Sensing of Continuum Robots: A Review.Sensors (Basel, Switzerland) · 2024Review
- Editorial: Design, modeling and control of kinematically redundant robots.Frontiers in robotics and AI · 2024Article
- Brain-inspired biomimetic robot control: a review.Frontiers in neurorobotics · 2024Review
- Deep CNN-Based Static Modeling of Soft Robots Utilizing Absolute Nodal Coordinate Formulation.Biomimetics (Basel, Switzerland) · 2023Article
- Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots.Sensors (Basel, Switzerland) · 2023Article
- Reduced order modeling and model order reduction for continuum manipulators: an overview.Frontiers in robotics and AI · 2023Review
- Characterization of continuum robot arms under reinforcement learning and derived improvements.Frontiers in robotics and AI · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots' inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges.
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.