Benchmarking Generative Trajectory Models for Active-Inference Control

arXiv:2610.05692v1 Announce Type: new Abstract: Learning from trajectory demonstrations offers a route to active-inference control of complex systems whose dynamics are difficult to model explicitly. We introduce generative active-inference control (GenAIF), in which one generative trajectory model learns from demonstrations and measured action interventions to supply a goal-conditioned policy distribution and a state-to-observation likelihood mapping. From this control design, we derive three model requirements: (i) useful action proposals, (ii) accurate prediction under imposed actions, and (iii) probabilistic observation evidence for belief updating and expected information gain. We benchmark diffusion, autoregressive Transformers, conditional variational autoencoders (CVAEs), and flow matching in a MuJoCo manipulation task with multiple physical conditions. Diffusion delivers the strongest control across the tested dynamics, while CVAE combines comparable short-horizon prediction with much faster inference. Correct conditioning is decisive, and trajectory reuse offers further computational savings. With the same frozen models, a hidden-dynamics experiment demonstrates prompt belief adaptation after an unannounced tilt change; subsequent instability identifies sustained inference as a remaining challenge. These findings support the use of shared generative trajectory models to connect action proposal, controlled prediction, and observation evidence within GenAIF.

Abstract:Learning from trajectory demonstrations offers a route to active-inference control of complex systems whose dynamics are difficult to model explicitly. We introduce generative active-inference control (GenAIF), in which one generative trajectory model learns from demonstrations and measured action interventions to supply a goal-conditioned policy distribution and a state-to-observation likelihood mapping. From this control design, we derive three model requirements: (i) useful action proposals, (ii) accurate prediction under imposed actions, and (iii) probabilistic observation evidence for belief updating and expected information gain. We benchmark diffusion, autoregressive Transformers, conditional variational autoencoders (CVAEs), and flow matching in a MuJoCo manipulation task with multiple physical conditions. Diffusion delivers the strongest control across the tested dynamics, while CVAE combines comparable short-horizon prediction with much faster inference. Correct conditioning is decisive, and trajectory reuse offers further computational savings. With the same frozen models, a hidden-dynamics experiment demonstrates prompt belief adaptation after an unannounced tilt change; subsequent instability identifies sustained inference as a remaining challenge. These findings support the use of shared generative trajectory models to connect action proposal, controlled prediction, and observation evidence within GenAIF.

Accepted at the 7th International Workshop on Active Inference (IWAI 2026). 16 pages, including appendix and references; 4 figures. Code: this https URL

Robotics (cs.RO)

arXiv:2610.05692 [cs.RO]

(or arXiv:2610.05692v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05692 arXiv-issued DOI via DataCite (pending registration)

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Benchmarking Generative Trajectory Models for Active-Inference Control

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