A Cable-Driven Soft Robotic Hand With an In-Hand RGB-D Camera for Dexterous Grasping and Manipulation
Bibliographic record
Abstract
The aspiration to replicate the capabilities of the human hand has driven innovations in the design of soft robotic hands. Despite these advancements, many existing designs of soft hands still lack effective in-hand vision and the ability for each finger to achieve active multi-degree-of-freedom motion. This paper proposes a cable-driven soft robotic hand that can achieve dexterous grasping and manipulation, vision-guided grasping, vision-based slip detection and compensation, as well as visually servoed in-hand manipulation. The hand has five soft fingers, each capable of independent flexion/extension motion and bidirectional ad/abduction motion. A red-green-blue-depth (RGB-D) camera is integrated into the palm of the soft hand to enable in-hand vision capability. Modeling of the soft hand is established to analyze its kinematics, statics, and manipulability. A series of experiments are conducted to demonstrate its dexterous grasping and manipulation capabilities on a variety of objects. Using 3D point cloud data from the in-palm camera, an effective vision-guided grasping strategy is developed to grasp objects on a table. The in-hand vision also enables slip detection and compensation during grasping to maintain the grasp stability. Furthermore, a hierarchical, visually servoed controller is developed to perform closed-loop in-hand object manipulation. With its high dexterity and visual feedback capabilities, the soft hand will find important applications such as household object manipulation and food picking/sorting, and may also be used as a prosthetic hand or an auxiliary hand for humans.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".