Cross-Modal Diffusion for Biomechanical Dynamical Systems Through Local Manifold Alignment

We present a mutually aligned diffusion framework for cross-modal biomechanical motion generation, guided by a dynamical systems perspective. By treating each modality, e.g., observed joint angles ($X$) and ground reaction forces ($Y$), as complementary observations of a shared underlying locomotor dynamical system, our method aligns latent representations at each diffusion step, so that one modality can help denoise and disambiguate the other. Our alignment approach is motivated by the fact that local time windows of $X$ and $Y$ represent the same phase of an underlying dynamical system, thereby benefiting from a shared latent manifold. We introduce a simple local latent manifold alignment (LLMA) strategy that incorporates first-order and second-order alignment within the latent space for robust cross-modal biomechanical generation without bells and whistles. Through experiments on multimodal human biomechanics data, we show that aligning local latent dynamics across modalities improves generation fidelity and yields better representations.

Continual Imitation Learning for Prosthetic Limbs

Lower limb amputations and neuromuscular impairments severely restrict mobility, necessitating advancements beyond conventional prosthetics. Motorized bionic limbs offer promise, but their utility depends on mimicking the evolving synergy of human movement in various settings. In this context, we present a novel model for bionic prostheses' application that leverages camera-based motion capture and wearable sensor data, to learn the synergistic coupling of the lower limbs during human locomotion, empowering it to infer the kinematic behavior of a missing lower limb across varied tasks, such as climbing inclines and stairs. We propose a model that can multitask, adapt continually, anticipate movements, and refine. The core of our method lies in an approach which we call -- multitask prospective rehearsal -- that anticipates and synthesizes future movements based on the previous prediction and employs a corrective mechanism for subsequent predictions. We design an evolving architecture that merges lightweight, task-specific modules on a shared backbone, ensuring both specificity and scalability. We empirically validate our model against various baselines using real-world human gait datasets, including experiments with transtibial amputees, which encompass a broad spectrum of locomotion tasks. The results show that our approach consistently outperforms baseline models, particularly under scenarios affected by distributional shifts, adversarial perturbations, and noise.