Looped World Models
Facemind / research
World-model architecture using looped transformer refinement over latent states for iterative environment understanding.
Data efficiency
Important trend signal for why OpenBot should model feedback and correction, not only observations.
A model hub link is a declaration. Files, loadability, evaluation, and deployment are scored separately.
Model decision scorecard
Use-case scores and evidence confidence are separate; Unknown is not treated as failure.
Code, weights, and checkpoints
UnknownArtifact availability is unknown.
Loading and training reproducibility
UnknownNo verified loading configuration is available.
Training data requirements
UsefulRequired signal categories are declared; exact tensor and action interfaces still need verification.
Evaluation evidence
UsefulEvaluation focus is declared, but metrics are not independently verified.
Deployment readiness
UnknownHardware, latency, dependencies, and runtime loading are not yet verified.
Artifact facts and provenance
No metadata-verified artifact facts yet. Source links remain declarations only.
Loop signal demand
Signals this model family needs for training, evaluation, or failure mining.
Observation / ego video
observation · Temporal egocentric and human-centric observations
Language intent / task phase
World Model
Action / robot state
Needs executable actions, robot state, hand pose, or trajectory supervision.
Future state / dynamics
future state · Benchmarks that measure data efficiency and rollout stability · Whether richer loop traces reduce prediction drift
Feedback / correction / failure
feedback · Dense signals that expose feedback and state correction
Sim-real / embodiment metadata
Needs embodiment, calibration, or sim-real pairing metadata for transfer analysis.
Evaluation focus
- Data efficiency
- Long-horizon latent-state stability
- Whether richer loop traces reduce prediction drift
Missing critical loop signals
Core signal demands are represented. Check quality, alignment, and access constraints.
Related catalog datasets
Ego4D
Large-scale daily-life egocentric video
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
Ego-Exo4D
Synchronized first-person and third-person skilled activity
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
Xperience-10M
Multimodal human experience for embodied AI
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
OpenBot notes
- Important trend signal for why OpenBot should model feedback and correction, not only observations.
- Pairs well with dataset coverage fields for gaze, intent, and feedback signals.