Error-State Model Predictive Path Integral Control of Tendon-Driven Continuum Robots using Cosserat Rod Dynamics with Strain Parametrization
Bibliographic record
Abstract
This paper presents a new error-state Model Predictive Path Integral (MPPI) framework for tendon-driven continuum robots (TDCRs). Tracking-error dynamics are derived employing a Lie group formulation, which preserves full pose geometry. This formulation yields more precise error metrics and enhanced position and orientation control. A nonlinear Cosseratrod model with strain parameterization is used to represent a closed form of TDCR dynamics. Using advanced implementation and valid assumptions, the model updates in approximately 0.3 ± 0.3 ms. The model is calibrated using weight release and actuation-based experiments on tendon-driven robotic ablation catheters. The states of the model are generalized coordinates that are estimated using a nested optimization. The proposed algorithm parallelizes the generation and evaluation of control trajectories. Also, the MPPI controller compensates for actuation uncertainties. Each control trajectory is sampled from the current best control trajectory, and the trajectory's cost function combines a baseline performance term with an adaptive uncertainty penalty. The latter describes measurement uncertainty. Then an exponential weighting scheme prioritizes lower-cost control trajectories. Also, the tendon displacement is used for the actuation and is acquired through an optimization that bypasses the need for force sensing. The framework can be employed for advanced medical TDCR applications such as cardiac ablation. The control algorithm is compared with a conventional Model Predictive Controller (MPC) via experiments. The results show that the proposed MPPI-based approach results in higher precision and greater computational efficiency than MPC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".