SayCan
Google Research / Everyday Robots
Language-grounded robotics approach that combines language-model planning with affordance scores from robot skills.
Instruction decomposition
Important for tagging datasets with task phase and feedback, not just final action traces.
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
Language intent / task phase
language intent · task phase · Task instructions decomposed into executable skill phases · Failure labels showing where language plans diverge from physical capability
Action / robot state
actions
Future state / dynamics
Needs future-state supervision or rollout structure to validate predictive dynamics.
Feedback / correction / failure
feedback/failure · Failure labels showing where language plans diverge from physical capability
Sim-real / embodiment metadata
Policy
Evaluation focus
- Instruction decomposition
- Affordance grounding
- Mismatch between language feasibility and physical execution
Missing critical loop signals
Core signal demands are represented. Check quality, alignment, and access constraints.
Related catalog datasets
EPIC-KITCHENS-100
Unscripted kitchen actions from wearable cameras
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.
EgoWorld
Bimanual manipulation in LeRobot format
Dataset license restricts commercial use.
OpenBot notes
- Important for tagging datasets with task phase and feedback, not just final action traces.
- Useful bridge from language intent data to executable robot skills.