Publication
ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons
Lucas Schulze; Maximilian Schwarz; Jona Hoppe; Jan Peters; Oleg Arenz
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2609.31434, Pages 1-8, arXiv, 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 dy-
namics 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.