ArticleFrontiers in robotics and AI2021
Using First Principles for Deep Learning and Model-Based Control of Soft Robots.
Article 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- Data-driven modeling and identification of a bistable soft-robot element based on dielectric elastomer.Frontiers in robotics and AI · 2025Article
- Advancements and Challenges in the Integration of Indium Arsenide and Van der Waals Heterostructures.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Soft touchless sensors and touchless sensing for soft robots.Frontiers in robotics and AI · 2024Review
- Modeling multi-contact point physical interaction between the anthropomorphic finger and soft robotic exo-digit for wearable rehabilitation robotics applications.Frontiers in robotics and AI · 2023Article
- Editorial: Machine Learning Techniques for Soft Robots.Frontiers in robotics and AI · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Model-based optimal control of soft robots may enable compliant, underdamped platforms to operate in a repeatable fashion and effectively accomplish tasks that are otherwise impossible for soft robots. Unfortunately, developing accurate analytical dynamic models for soft robots is time-consuming, difficult, and error-prone. Deep learning presents an alternative modeling approach that only requires a time history of system inputs and system states, which can be easily measured or estimated. However, fully relying on empirical or learned models involves collecting large amounts of representative data from a soft robot in order to model the complex state space-a task which may not be feasible in many situations. Furthermore, the exclusive use of empirical models for model-based control can be dangerous if the model does not generalize well. To address these challenges, we propose a hybrid modeling approach that combines machine learning methods with an existing first-principles model in order to improve overall performance for a sampling-based non-linear model predictive controller. We validate this approach on a soft robot platform and demonstrate that performance improves by 52% on average when employing the combined model.
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Registered trials
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