Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations. To address this challenge, we introduce a device-agnostic framework that combines physiologically plausible musculoskeletal simulation with reinforcement learning to enable scalable personalized exoskeleton assistance for both able-bodied and clinical populations. Our control policies not only generate physiologically plausible locomotion dynamics but also capture clinically observed compensatory strategies under targeted muscular deficits, providing a unified computational model of both healthy and pathological gait. Without task-specific tuning, the resulting exoskeleton control policies produce assistive torque profiles at the hip and ankle that align with state-of-the-art profiles validated in human experiments, while consistently reducing metabolic cost across walking speeds. For simulated impaired-gait models, the learned control policies yield asymmetric, deficit-specific exoskeleton assistance that improves both energetic efficiency and bilateral kinematic symmetry without explicit prescription of the target gait pattern. These results demonstrate that physiologically plausible musculoskeletal simulation via reinforcement learning can serve as a scalable foundation for personalized exoskeleton control across both able-bodied and clinical populations, eliminating the need for extensive physical trials.
Figures & tables
Fig. 1: Overview of the musculoskeletal simulation and learning framework. (a) Reference kinematics and joint moments are extracted from a biomechanics dataset [ 21 ] containing synchronized motion capture and force plate data using OpenSim [ 22 ] . The policy observes future reference kinematics and outputs muscle activations to imitate able-bodied human locomotion. The reward function is designed to track reference kinematics and moments while simultaneously minimizing energy expenditure via a metabolic cost probe. (b) Fine-tuning phase for the human-exoskeleton system. The action space is extended to include assistive torque motors alongside muscle activations to simulate exoskeleton assistance. (c) Fine-tuning phase for the impaired gait policy. An activation mask reduces the maximum excitation of targeted muscles to generate compensatory impaired gait patterns. Both conditions can be applied simultaneously to generate impaired-gait-specific exoskeleton assistance. For both downstream fine-tuning tasks, the framework uses lower kinematic tracking weights and a higher metabolic energy penalty to accommodate the induced physical changes.
Pelvis
Thigh
Shank
Foot
Total
(kg)
(kg, each)
(kg, each)
(kg, each)
(kg)
Hip Exo
1.5
0.7
–
–
2.9
Ankle Exo
1.5
–
1.0
0.2
3.9
TABLE I: Mass Distribution of Simulated Exoskeleton Devices
Base Model
Fine-Tuning
Component
k
w
k
w
Position (pos)
-2.0
0.25
-0.4
0.25
Velocity (vel)
-0.05
0.1
-0.01
0.1
Root position (root)
-500.0
0.15
-500.0
0.15
End-effector (ee)
-80.0
0.25
-16.0
0.25
Torque (torq)
-2.0
0.25
-0.4
0.25
TABLE II: Gain Constants ( k ) and Reward Weights ( w )
Fig. 2: Kinematic and kinetic tracking performance of the baseline policy across five locomotor speeds (walking and running). Blue and gray lines represent the simulated agent and the ground-truth data, respectively. Joint angles and moments for the hip, knee, and ankle are compared against the ground truth; ground reaction force is not included as a reference signal during training. All values are segmented into gait cycles using heel contact events, interpolated to 100 data points, and averaged over 10 cycles. A fourth-order zero-lag Butterworth low-pass filter with a 4 Hz cutoff frequency is applied to both the ground-truth and simulated joint moment signals to reduce noise.
Parameter
Value
Network Size (Actor / Critic)
[512, 512, 256]
Batch Size
256
Learning Rate
Linear decay from 3×10−4
Soft Update Coefficient
0.02
Entropy Coefficient
auto
Discount Factor
0.95
TABLE III: SAC Hyperparameters
Fig. 3: Generated exoskeleton assistive torque profiles (Nm/kg, normalized by model mass) for the hip and ankle joints during walking and running. The assistance profiles from the fine-tuned policy (blue gradients, ranging from 0.6 to 2.2 m/s) are compared against experimentally optimized torque profiles from prior work [ 13 , 39 , 40 ] . Reference profiles for running with a hip exoskeleton consist of optimized extension-only splines, while reference ankle profiles represent optimized plantarflexion-only splines.
Fig. 4: Gross metabolic cost (W/kg) of the generated exoskeleton control policies for the able-bodied model. (a) Metabolic cost across locomotion speeds for the no-exoskeleton baseline (gray), hip assistance (blue), and ankle assistance (green) conditions. (b) Average metabolic cost during walking and running. All values are computed using the metabolic cost probe.
Fig. 5: Kinematic and kinetic deviations of simulated impaired gaits (orange) relative to the able-bodied baseline (blue). Top row: joint angles and moments for the affected side of the model with simulated ankle plantarflexor weakness, characterized by excessive dorsiflexion and a reduced plantarflexion push-off moment. Bottom row: results for the model with simulated hip flexor weakness, exhibiting a compensatory hip-hiking strategy and a corresponding reduction in the biological hip flexion moment.
Fig. 6: Exoskeleton assistive torque profiles for the affected and non-affected sides of the impaired-gait models at a walking speed of 1.2 m/s. In the model with ankle plantarflexor weakness (left), peak plantarflexion assistance on the affected side (orange) is 59% greater than on the non-affected side (dark blue). In the model with hip flexor weakness (right), peak hip flexion assistance on the affected side is 49% greater.
Fig. 7: Effect of impaired-gait-specific exoskeleton assistance on kinematics and energy expenditure at a walking speed of 1.2 m/s. (a) Joint kinematics of the affected (orange) and non-affected (blue) sides for models with ankle plantarflexor weakness (top) and hip flexor weakness (bottom). (b) Gross metabolic cost for simulated impaired gait with exoskeleton assistance (green) compared to the no-exoskeleton baseline (gray).
Division of Industrial Data Science (DIDS), School of Data Science, Lingnan University, Hong Kong · Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China