Adaptive solution to transfer learning of neural network controllers from earth to space environments
Bibliographic record
Abstract
Abstract Compliant manipulation has long been a major constraint for grappling in robotic manipulators. To adopt robotic manipulators in space for the prospect of capturing space junk and transforming them into salvageable assets for re‐use, robust adaptive manipulation would be key. We believe that a bio‐inspired approach could provide human‐like tactility required for robustness and adaptability in robotic manipulation. Given the similarity in form and dynamics between earth‐based and space‐based robotic manipulators, we first explored the transfer learning of neural network controllers as an avenue to address the challenges of limited computation resources onboard the spacecraft (space manipulator). We introduced a pre‐trained and learned feedforward neural network for modelling the control error a priori. While the results were encouraging, there are major limitations of neural networks' capability to ensuring the transfer learning of similar earth‐based dynamics to space‐based dynamics, given that the parameters of contrast are fairly straightforward. We have demonstrated these limitations by presenting a novel approach that is inspired by human motor control. We explored the adaptability through a practical problem of transferring a neuro‐controller from earth to space. With the results not as plausible as expected, an alternative adaptive controller has been learned to demonstrate a viable solution. The controller was trained entirely in simulation via rapid online adaptation of the robot's controller to the object's properties and environmental dynamics using only proprioception history. As a notable step, we have shown that appropriate models can be learned in this manner by training the control policy via reinforcement learning, which provides avenue for transferring the learned model from earth to space environments.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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".