RDT-1B
Robotics Diffusion Transformer research
Diffusion foundation model for bimanual manipulation that uses large-scale robot data to generate action trajectories.
Bimanual manipulation success
Useful for showing why action/state tags need to distinguish single-arm, bimanual, and dexterous data.
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
UsefulAn official model hub is linked; weight files and loadability are not verified.
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 · Bimanual demonstrations with synchronized visual observations and action/state traces
Language intent / task phase
language intent · Language or task conditioning for manipulation goals
Action / robot state
actions · robot state · trajectory · Bimanual demonstrations with synchronized visual observations and action/state traces
Future state / dynamics
Policy
Feedback / correction / failure
Contact-rich failure and recovery cases for robust long-horizon behavior · Bimanual manipulation success
Sim-real / embodiment metadata
robot state · Sensitivity to embodiment/action-space mismatch
Evaluation focus
- Bimanual manipulation success
- Long-horizon action generation
- Sensitivity to embodiment/action-space mismatch
Missing critical loop signals
Core signal demands are represented. Check quality, alignment, and access constraints.
Related catalog datasets
OBayData Dexterous Manipulation Demo
Egocentric dexterous manipulation sample set
Exact action dimensions, control frequency, normalization, and camera mapping require interface verification.
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.
OpenBot notes
- Useful for showing why action/state tags need to distinguish single-arm, bimanual, and dexterous data.
- Open weights make it a practical candidate for a future OpenBot model-readiness benchmark.