RT-X
Open X-Embodiment collaboration
Cross-embodiment RT model family trained from the Open X-Embodiment mixture to study transfer across robots and tasks.
Cross-embodiment transfer
Important for OpenBot because dataset lineage and embodiment metadata become training variables.
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 · Multi-robot trajectories with normalized observations and actions · Embodiment metadata that preserves robot, camera, and action-space differences
Language intent / task phase
language intent · Task success under new robot/action spaces
Action / robot state
actions · robot state · Multi-robot trajectories with normalized observations and actions · Embodiment metadata that preserves robot, camera, and action-space differences
Future state / dynamics
Needs future-state supervision or rollout structure to validate predictive dynamics.
Feedback / correction / failure
Cross-dataset evaluation to measure whether scaling mixtures improves transfer · Task success under new robot/action spaces
Sim-real / embodiment metadata
robot state · embodiment · Multi-robot trajectories with normalized observations and actions · Embodiment metadata that preserves robot, camera, and action-space differences
Evaluation focus
- Cross-embodiment transfer
- Dataset mixture quality
- Task success under new robot/action spaces
Missing critical loop signals
Core signal demands are represented. Check quality, alignment, and access constraints.
Related catalog datasets
EgoWorld
Bimanual manipulation in LeRobot format
Dataset license restricts commercial use.
MicroAGI01
Household manipulation with pose annotations
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
Egocentric Adjust Bottle
Apache-2.0 LeRobot bottle-adjustment task
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
OpenBot notes
- Important for OpenBot because dataset lineage and embodiment metadata become training variables.
- Pairs well with Catalog fields for format, access, action/state, and sim-real alignment.