Structured Multitask Gaussian Processes for Probabilistic Full-Body Human Motion Prediction

Abstract:Robots that share space with people need human motion predictions with reliable uncertainty, not just accurate means. This paper presents a structured multitask variational Gaussian process (GP) for probabilistic full-body human motion prediction. The model integrates one-shot prediction of the full horizon, multitask outputs that couple future time steps, a factorization into one sparse variational GP per joint-dimension pair of the rotation representation, and a continuous 6D rotation representation with forward kinematics that preserves bone lengths. Its predictive distribution is available in closed form: a Gaussian with full temporal covariance for each rotation coordinate, which downstream planners operating in rotation coordinates can use without sampling, density estimation, or forward kinematics. On Human3.6M, the model achieves lower kernel-density-estimated negative log-likelihood (KDE NLL) than Motron and DLow at every prediction step, 22-52 nats below Motron. Empirical coverage is conservative at the 50% and 80% levels and close to nominal at 95% over a 2 s horizon. The probabilistic model uses 0.24 M parameters, about seven times fewer than Motron, and a separately trained 0.35 M-parameter deterministic variant has a mean angle error 3-22% higher than Motron's. Ablations confirm the benefit of the 6D representation and show that, at a fixed training budget, factorized joint outputs outperform coupled right-arm alternatives in NLL and CRPS while using less memory. These results show that compact, structured GPs provide analytic, temporally structured uncertainty with conservative empirical coverage for robotic systems.

Robotics (cs.RO)

arXiv:2603.07096 [cs.RO]

(or arXiv:2603.07096v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2603.07096 Focus to learn more arXiv-issued DOI via DataCite

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