[
    {
        "id": "osp-25834",
        "type": "article-journal",
        "title": "ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons",
        "author": [
            {
                "family": "Schulze",
                "given": "Lucas"
            },
            {
                "family": "Schwarz",
                "given": "Maximilian"
            },
            {
                "family": "Hoppe",
                "given": "Jona"
            },
            {
                "family": "Peters",
                "given": "Jan"
            },
            {
                "family": "Arenz",
                "given": "Oleg"
            }
        ],
        "URL": "https://omanscience.com/en/articles/exolan-physics-consistent-context-aware-dynamics-learning-for-exoskeletons",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Task-agnostic assistive exoskeleton control based on human intention offers greater flexibility than conventional approaches that rely on predefined tasks or motion patterns. Human joint torque estimation enables task-agnostic assistance by characterizing user actions. Physics-consistent methods such as Deep Lagrangian Networks (DeLaN) have been applied to estimate the human torques in multi-user settings, but existing approaches cannot adapt to a specific user without retraining, and do not account for intermittent contacts during locomotion. We propose ExoLaN, a Context-Aware DeLaN for human-exoskeleton interaction that learns the full coupled system dynamics while adapting to changes in interaction context. ExoLaN combines temporal context with partial contact-force measurements from force-sensitive insoles to infer latent dynamics embeddings and estimate generalized contact torques. On seven unseen users performing 21 unseen tasks, ExoLaN reduces torque estimation MSE by 7% compared to a black-box baseline. Beyond inverse dynamics, ExoLaN serves as a unified model that also enables accurate forward prediction: training with a multi-step prediction loss reduces acceleration MSE by 59% and long-horizon position and velocity errors by 60% and 93%, respectively, compared with a single-step loss. Moreover, the learned latent context captures task information without explicit task labels, making it a promising signal for task-aware assistive control."
    }
]