Embodiment-aware control by inference over the operator: a simulation study
Overview
This research introduces an embodiment-aware controller that infers operator embodiment and visuo-proprioceptive cue weighting from implicit gaze and pupil signals using a discrete Active Inference agent. In simulations with heterogeneous synthetic operators, the method increased embodiment scores from 0.76 to 1.66 on a 0-2 scale, reaching within 0.02 of an oracle across ten random seeds. Unlike prediction-error minimizers that withhold feedback or fixed-setting controllers, this approach adapts to operator needs while respecting explicit preferences. The model also reproduced known embodiment-performance relations in human data and developed embodiment on plausible time scales. Results indicate that the benefit stems from inference over the operator with explicit preferences rather than information seeking alone.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“Embodiment-aware control by inference over the operator: a simulation study”
Exact source · revision 1Source owner not reported
Reported quantity · embodiment score: 0.76 other · Basis not reported
“tive. 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”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalEmbodiment-aware control by inference over the operator: a simulation study
This research introduces an embodiment-aware controller that infers operator embodiment and visuo-proprioceptive cue weighting from implicit gaze and pupil signals using a discrete Active Inference agent. In simulations with heterogeneous synthetic operators, the method increased embodiment scores from 0.76 to 1.66 on a 0-2 scale, reaching within 0.02 of an oracle across ten random seeds. Unlike prediction-error minimizers that withhold feedback or fixed-setting controllers, this approach adapts to operator needs while respecting explicit preferences. The model also reproduced known embodiment-performance relations in human data and developed embodiment on plausible time scales. Results indicate that the benefit stems from inference over the operator with explicit preferences rather than information seeking alone.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).