SuRFR: A Fast Recursive Simulator for Soft Manipulators with Discrete Joints on SE(3)
Bibliographic record
Abstract
We develop a fast, recursive and parameterization-free formulation for forward and inverse dynamics of soft robots modelled as multi-body systems with rigid and flexible bodies. The system is composed of bodies serially connected with discrete single-degree-of-freedom joints from a fixed base. The joint kinematics is captured through the exponential map of the Special Euclidean group SE(3). We couple the Newton-Euler equation for rigid bodies and a set of Partial Differential Equations (PDEs) on the SE(3) for dynamic Cosserat rods to formulate the dynamics of multi-body systems. Our proposed inverse dynamics recursively determines the system’s response as well as the required joint torques to follow a prescribed joint-space trajectory; while the forward dynamics inherits the same structure and determines the system’s motion for given joint torques. Unlike rigid multi-body systems, where the kinematic and dynamic recursions are decoupled, the inclusion of flexible bodies necessitates solving a coupled set of PDEs with applied separated Boundary Conditions (BCs). We develop a shooting-method-based BC solver to move BCs to one point and a numerical framework to integrate these equations. The framework adopts a fast finite difference method to semi-discretize the PDEs and transform them into ordinary differential equations, which are integrated using a geometrically exact integrator on the SE(3). Subsequent to the validation against multiple packages, we implement the proposed algorithms and study the dynamic response of an example. A software library, named SimUlator for Rigid-Flexible Robots (SuRFR), to simulate generic rigid-flexible multi-body systems, including the presented example, is distributed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".