FARMS: Framework for Animal and Robot Modeling and Simulation
Open accessAbstract
The apparent ease with which animals move hides the complexity of the neuromechanical systems underlying locomotion. This behavior remains difficult to understand and replicate, motivating sustained interest across biology, neuroscience, robotics, and computer animation. Despite extensive progress, existing approaches often focus on isolated aspects of locomotion, limiting the integration of neural control, biomechanics, and environmental interactions within a unified modeling framework. Bridging biological insight, robotic implementation, and physically based character animation therefore requires shared, extensible computational tools that promote reuse, while supporting physically grounded modeling and control across animals and robots. Our key technical contribution is the development of FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary Python framework that integrates and extends existing open-source software for 3D modeling, experiment design, optimization, and data analysis within a modular and extensible workflow. FARMS lowers the barrier for constructing and simulating whole-body animal and robot models, enabling the definition of skeletal structures, musculature, and neural circuits at multiple levels of abstraction within a unified workflow. FARMS has already been used to model and study locomotor behaviors in a variety of animals and robots, and we demonstrate the framework’s versatility and effectiveness through a series of simulation case studies. We show that FARMS enables the reuse of model components across morphologies of different complexities, while capturing mechanical interactions between the body and diverse environments, including terrestrial and aquatic settings. This allows the study and synthesis of walking, swimming, and locomotor transitions within a single, unified framework.
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Evidence for each of these control approaches has been found across species, motivating their application toward robotic motor control.
“Evidence for each of these control approaches has been found across species 58,59,60,61 , motivating their application toward robotic motor control 62,63 .”
From NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila (2024) · cites this paper as [59]
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Self-citation
- Authors
- Arreguit, J., Tata Ramalingasetty, S., Danner, S. M., & Ijspeert, A.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Department of Neurobiology and Anatomy, College of Medicine, Drexel University, Philadelphia, PA, USA
- Corresponding author
- Arreguit, Jonathan
- Ijspeert, Auke
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2023-09-26
- PMC
- PMC10827226
- OpenAlex
- W4387037880
- Cited by
- 12
- References
- 79
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS112304, R01NS115900)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Robotic Locomotion and Control
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- farms, Animal locomotion, robot locomotion, neuromechanics, musculoskeletal modeling, neural control, biomechanics, physics-based animation, open-source simulation framework, walking, swimming, aquatic locomotion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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