Deep Learning-based Robot Control using Recurrent NeuralNetworks (LSTM; GRU) and Adaptive Sliding Mode Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A phenomenal increase in computational power made deep learning possible for real-time applications in recent years. Nonlinearity, external disturbances, and robustness are significant challenges in robotics. To overcome these challenges, robust adaptive control is needed, which requires manipulator inverse dynamics. Deep Learning can be used to construct the inverse dynamic of a manipulator. In this paper, robust adaptive motion control is developed by effectively combining existing adaptive sliding mode controller (ASMC) with Recurrent Neural Network such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). A supervised learning approach is applied to train the LSTM and GRU model, which replaced the inverse dynamic model of a manipulator in model-based control design. The LSTM-based inverse dynamic model constructed using input-output data obtained from a simulation of a dynamic model of the two-links robot. The deep-learning-based controller applied for trajectory tracking control, and the results of the proposed Deep Learning-based controller are compared in three different scenarios: ASMC only, LSTM or GRU only, and LSTM or GRU with ASMC (with and without disturbance) scenario. The primary strategy of designing a controller with LSTM or GRU is to get better generalization, accuracy enhancement, compensate for fast time-varying parameters and disturbances. The experimental results depict that without tuning parameters proposed controller performs satisfactorily on unknown trajectories and disturbances.
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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 it