Arm-Hand Systems As Hybrid Parallel-Serial Systems: A Novel Inverse Kinematics Solution
Bibliographic record
Abstract
In this paper, we aim to solve inverse kinematics of the integrated robotic arm-hand systems to achieve precision grasping, provided the desired grasp configuration (contact points + contact normals). The key insights of our approach are three-fold. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the "thumb" to narrow down the search space and increase the success rate of the algorithm. Second, we formulate the arm-thumb serial chain as a closed chain while other fingers are still as serial chains such that the entire arm-hand system is controlled as a hybrid parallel-serial system. The closed-chain formulation truncates and simplifies the task hierarchy of the entire arm-hand system. Third, we attach a virtual revolute joint to the thumb’s tip with its rotation axis aligning with the thumb’s contact normal to allow this virtual joint to act as the embodiment of the thumb’s functional redundancy. By selecting the thumb’s joints including the virtual revolute joint as the active joints of the arm-thumb closed chain, the arm-thumb system’s self-motion (i.e., the palm pose) and the thumb’s functional redundancy can be directly controlled without using the null space projection. This provides a new possibility to control the self-motion of robot manipulators. Simulation results will demonstrate the advantages and superb performance of the proposed approach for solving the problem of inverse kinematics of achieving precision grasps compared to other classical approaches based on the Damped Least-Squares method [1] in terms of the average success rate (96% v.s. 12%).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".