Bio-Inspired Adaptive Control of Robotic Manipulators for Grasping in Orbital Space Missions
Bibliographic record
Abstract
This research addresses the control problem of spaceborne manipulators for adaptive and reactive grappling of space targets for orbital missions requiring faster dynamics interactions than are currently employed.One of the major difficulties in validation of space manipulator algorithms is the challenge and expense of experimentally replicating the space environment on earth.This research's approach to solving this problem has evolved from a bio-inspired feedforward approach to a proprioceptive reinforcement learning approach.Initially it was assumed that a bio-inspired feedforward approach could provide human-like tactility required for robustness and adaptability in space robotic manipulation.A cerebellum-inspired pre-trained neural network has been implemented as a forward model as a means of circumventing potential problems with traditional feedback controllers for space manipulators.Such problems are anticipated in sophisticated real-world applications of space manipulation such as the manipulatormounted free-flyer spacecraft.Cerebellar-inspired forward models have thus far been demonstrated in only simple proof-of-concept problems.A novel method was developed for combining the multi-layer perceptron (MLP) with a multi-output regression tree (MORT) in a pre-trained feedforward neural network capable of predicting forward trajectories to an accuracy of 89-96% for previously unseen trajectory data.Given the similarity in form and dynamics between earth-based and space-based This thesis is dedicated to my lovely wife (Aretha), son (Timi), and my dear parents.Many thanks to my wife for her show of support, prayers, and understanding, throughout this academic journey.I would like to thank my siblings
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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) | 0.001 | 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".