Design and Construction of a Cost-Effective Dexterous Robotic Hand for Research and Development
Bibliographic record
Abstract
Human-Robot Interaction (HRI) is increasingly becoming commonplace in various fields, including service robotics, industrial automation, and healthcare. Public acceptance and positive interaction with these robots are heavily influenced by the appearance and human likeness of these robots. To enable interaction with human beings, robots need manipulators that support high dexterity, cost-efficiency, and ease of development. This study presents the design and development of EvoGrip, a humanoid dexterous robotic hand aimed at supporting research and development in HRI. EvoGrip is a cost-effective and easy-to-build robotic hand, adapted from the open-source project Inmoov, enhanced with actuators, sensors and other added value features. The mechanical design changes to the hand include improvements for movement repeatability and the addition of a degree of freedom in the thumb to enable greater dexterity. The development and integration of a custom string potentiometer system, enables precise finger position tracking while simultaneously reducing design complexity. Furthermore, the study develops two distinct modeling approaches for EvoGrip's finger dynamics: a mathematical model based on first principles and a data-driven model using system identification techniques. Both modeling strategies demonstrated high accuracy, with the system identification model showing superior performance in compensating for complex, nonlinear behaviors. This work establishes a foundation for future research and advancements in robotic hands, focusing on real-time control, advanced pressure excursion and applications in human-centric tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".