Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
Bibliographic record
Abstract
Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".