Wearable assistive devices that monitor muscle fatigue reduce the risk of work-related musculoskeletal disorders, enhance rehabilitation outcomes, and extend operational time by optimizing the power consumption of the device. This work proposes a muscle fatigue-aware controller (MFAC) for a semi-rigid knee exoskeleton. During an offline calibration phase, we use Gaussian Process Regression (GPR) to model the relationship between muscle activation (measured via EMG) and the corresponding joint moment and angle, enabling fatigue state estimation for the controller. The trained model then approximates muscle activation online using only joint states and moment derived from user’s kinematic data and ground reaction forces provided by the wearable device. The estimated muscle activation is used to assess the muscle fatigue state through a model-based fatigue evaluation module. Notably, EMG measurement is only required during the offline training in our approach, enabling EMG-free online estimation, which significantly enhances the feasibility for long-term mobile applications. Building on muscle fatigue and human-exoskeleton interaction models, we then developed an adaptive controller within a predictive control framework. The resulting optimization problem generates control signals that adjust assistance to reduce the fatigue progression. Two experiments validate the EMG-free fatigue estimation method and the integrated MFAC, demonstrating accurate muscle activation estimation and effective adaptive assistance based on the estimated fatigue state. Analysis of actuator power output reveals adaptivity in which the controller conserves energy during low muscle fatigue and progressively improves power output with increasing fatigue, suggesting a longer duration of the device with a fixed battery capacity.

Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between quality and diversity limits the performance of these methods. We introduce the Phasic Diversity Optimization (PDO) algorithm, a Population-Based Training framework that separates reward and diversity training into distinct phases instead of optimizing a multi-objective function. In the auxiliary phase, agents with poor performance diversified via determinants will not replace the better agents in the archive. The decoupling of reward and diversity allows us to use an aggressive diversity optimization in the auxiliary phase without performance degradation. Furthermore, we construct a dogfight scenario for aerial agents to demonstrate the practicality of the PDO algorithm. We introduce two implementations of PDO archive and conduct tests in the newly proposed adversarial dogfight and MuJoCo simulations. The results show that our proposed algorithm achieves better performance than baselines.