LSTM-Based Visual Control for Complex Robot Interactions
Bibliographic record
Abstract
This paper introduces a novel application of the Long Short-Term Memory (LSTM) recurrent neural network for the identification and control of complex systems.The computation-intensive task of calculating the interaction matrix, a necessity for visual control laws in these systems, makes LSTM a fitting solution.The proposed control law unfolds in two phases: an offline phase, where the LSTM is trained on a set of visual features to generate a kinematic screw vector, and an online phase, where the trained LSTM is utilized for real-time system control.To assess the efficacy of the LSTM-based approach, we undertook a case study involving a manipulator robot, the UR5.We executed a series of simulations under various conditions to illustrate the effectiveness of the proposed LSTM-based control law.The outcomes from these experiments affirm the robustness of the LSTM controller, outperforming traditional methods even when faced with rapid fluctuations in visual features, partial loss of visual information, and model uncertainties in the robot.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".