arXiv:2609.38437 · v2
Embodiment-aware control by inference over the operator: a simulation study
基于对操作者推断的具身感知控制：一项仿真研究
摘要
Teleoperation and assistive devices are tuned for channel fidelity, while whether the operator experiences the device as part of the body, the Sense of Embodiment, is measured only afterwards. Minimizing the mismatch between the operator's predictions and the returned feedback fails twice: the predictions cannot be observed, and removing feedback minimizes the objective. We formulate an embodiment-aware controller that instead infers the operator's embodiment and visuo-proprioceptive cue weighting from implicit gaze and pupil signals and task outcome, and chooses bounded device settings under explicit preferences, cast as a discrete Active Inference agent. In simulations with heterogeneous synthetic operators, it raised embodiment from 0.76 to 1.66 on a 0-2 scale over the best fixed setting, within 0.02 of an oracle across ten random seeds; adaptation that did not read the operator gained nothing, and a prediction-error minimizer withheld feedback. An expected-utility controller with the same inference did as well, so the benefit comes from inference over the operator with explicit preferences, not from information seeking. Rules on a single signal came close when that signal was informative and failed when it was not; the controller degraded less, but failed when it trusted an uninformative signal unless briefly calibrated. Against published human data, the model reproduced the embodiment-performance relation, and embodiment developed on a plausible time scale. The embodiment signals were redundant when task success tracked embodiment and became the controller's objective when it did not. Studies with people must first establish calibrated cue-weighting signals and effort-independent embodiment signals.
作者备注：This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 20 pages, 4 figures, 7 tables. Code and data: https://github.com/sa-falcone/uee-embodiment-sim (doi:10.5281/zenodo.24106761)