Staged Multi-Agent Training (SMAT) for Hip Exoskeletons: Metabolic and Biomechanical Validation of a Simulation-Trained Co-Adaptive Controller

Learning-based controllers can deliver exoskeleton assistance after training entirely in physics-based simulation, yet few controllers that address human-device co-adaptation have been validated on real users by whole-body metabolic measurement, the standard benchmark for assistive walking. Co-adaptation is challenging: as the device alters joint dynamics, the wearer reorganizes neuromuscular coordination, producing a non-stationary learning problem. Staged Multi-Agent Training (SMAT), a four-stage curriculum that progressively trains a musculoskeletal human actor and a bilateral hip exoskeleton actor, was introduced and shown to reduce simulated hip-muscle activation and provide positive assistance on hardware. This article provides the first physiological validation of SMAT. The policy was deployed on a hip exoskeleton and tested with eight healthy adults, with metabolic cost measured by indirect calorimetry across no-exoskeleton, passive, and active conditions. Active assistance lowered net metabolic rate by 19.7% relative to the passive device (p < 0.001). Biomechanical analysis confirmed predominantly positive hip mechanical power across all subjects (positive-power ratio 0.98), and the policy generalized across walking speeds and terrains. Together, these results show that a single simulation-trained SMAT policy, deployed without subject-specific retraining, delivers a significant metabolic benefit on real users while remaining robust beyond the conditions it was trained on.

OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons

Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that cannot adapt to dynamic real-world environments or individual user characteristics. We present OLIVE (Online Low-rank Incremental Learning for Efficient Adaptive Exoskeletons), a parameter-efficient online adaptation framework that continuously personalizes exoskeleton control during deployment. OLIVE decomposes the adaptive component of the control policy into a low-rank residual form $ΔW = A_t B_t^\top$ with rank $r \ll \min(d,k)$, reducing the online update cost from $\mathcal{O}(dk)$ to $\mathcal{O}(r(d+k))$ while preserving the stability of a pretrained base controller $W_0$. Parameters are updated through a reward-shaped policy gradient driven purely by on-body sensor feedback, including EMG, IMU, and vibration signals, eliminating dependence on offline reference trajectories. A gating mechanism modulates the strength of personalization based on the contextual state, while a dynamic rank scheduler adapts the update dimensionality to terrain complexity. It allocates minimal capacity on simple flat terrain and expands to higher-rank updates on demanding uneven surfaces, enabling robust performance across flat walking, stair navigation, slopes, and uneven terrain. Experiments on the wearable platform demonstrate that OLIVE achieves improvements of 13, 22, and 15 percentage points in gait smoothness, effort reduction, and motion stability over the strongest baseline, respectively. It converges within approximately 1,800 walking steps with an end-to-end latency of 7.4 ms. Our code implementation is available at https://github.com/FastLM/OLIVE.