iLeAD: An EMG-Based Adaptive Shared Control Framework for Exoskeleton Assistance via Deep Reinforcement Learning
Bibliographic record
Abstract
This paper introduces the Intelligent Learning Assistive Devices (iLeAD) framework, a shared control architecture for an elbow exoskeleton that adapts in real time to changing external conditions using deep reinforcement learning (RL). iLeAD employs a Latent Guidance Encoder (LGE) to encode shoulder configurations and external loads into latent variables, guiding the Exoskeleton Control Policy (ExoCoP) to provide adaptive torque assistance. An Online Latent Estimator (OLE), trained via knowledge distillation, enables the exoskeleton to continuously infer these latent factors from its own observations. To model human arm motion, a separate RL-based Musculoskeletal Control Policy (MusCoP) generates muscle activations, which we validate against static optimization (SO) and computed muscle control (CMC) in a high-fidelity musculoskeletal simulation. All experiments are performed in this simulation environment, demonstrating that iLeAD achieves precise elbow tracking and robust adaptation to dynamic loads and shoulder configurations. These results highlight a promising approach for intuitive, effective human-exoskeleton interaction and advance the potential for practical human power augmentation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".