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EO-1

IPEC / EO-Robotics

A unified embodied model using interleaved vision-text-action pretraining for perception, reasoning, planning, and continuous robot control.

Code
Not listed
Weights
Available
Checkpoint
Available
License
mit

Decision summary

Best for

Unified reasoning and action

Main blocker

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

Next check

Run a checkpoint loading smoke test before marking the model pipeline-tested.

Evidence profile

A visual read of adoption evidence. Scores describe catalog evidence readiness, not task performance.

Published evaluation

5/6

dimensions scored

Unknown evidence remains visible and is never treated as a zero.

Access and governance75conf. 85
Artifact availability90conf. 90
Training and loading reproducibility70conf. 65
Training data requirements76conf. 70
Evaluation evidenceNot scoredconf. 10
Deployment readiness70conf. 55

Why these scores

The strongest decision reasons behind the evidence profile.

Top 3 adoption signals

Artifact availability

Useful

Checkpoint files were verified in the official repository metadata.

90 · confidence 90

Training and loading reproducibility

Useful

Configuration files are present; runtime loading is not tested.

70 · confidence 65

Evaluation evidence

Unknown

No structured evaluation evidence has been verified.

· confidence 10

Data requirements

Declared loop-data needs, missing evidence, and the next checks that matter.

4 required categories

Observation / ego video

observation

required

Language intent / task phase

language intent · task phase

required

Action / robot state

actions

required

Future state / dynamics

future state

required

Critical gaps

Core categories are represented. Interface alignment and data quality still require verification.

Linked datasets

Signal-level links only. Verify runtime interfaces before use.

Browse datasets
More evidenceShow

Additional data signals

Sim-real / embodiment metadata

Descriptive model record

useful

Additional decision dimensions

Access and governanceUseful

Access and license are declared by the source.

75 · c85
Training data requirementsUseful

Required signal categories are structured; exact tensor and action interfaces still need verification.

76 · c70
Deployment readinessUseful

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

70 · c55

Artifact facts

release_year
official_claim
2025
curated official source year
model.required_signals
official_claim
observation · language intent · task phase · actions · future state
Official model documentation and curated signal mapping
model.hub
official_claim
declared
curated model hub link
model.weight_files
metadata_verified
repository: IPEC-COMMUNITY/EO-1-3B · count: 2 · sample files: model-00001-of-00002.safetensors · model-00002-of-00002.safetensors
Hugging Face model metadata siblings
model.checkpoint_files
metadata_verified
repository: IPEC-COMMUNITY/EO-1-3B · count: 2 · sample files: model-00001-of-00002.safetensors · model-00002-of-00002.safetensors
Hugging Face model metadata checkpoint files
model.config_files
metadata_verified
repository: IPEC-COMMUNITY/EO-1-3B · count: 5 · sample files: config.json · generation_config.json · preprocessor_config.json · processor_config.json · tokenizer_config.json
Hugging Face model metadata siblings
license.weights
metadata_verified
mit
Hugging Face model card license metadata
license
metadata_verified
mit
Hugging Face model card license metadata
release_timing
metadata_verified
2025-08-28T17:26:15.000Z
arXiv 2508.21112 published
discovery.source
secondary_claim
name: worldbench/awesome-embodied-data-pyramid · trust: discovery_only · revision: 0568599d0619f20946e8089f6a941d0d9e30b690
#table-embodied-foundation-models-vla-wam, table 17, row 28
discovery.raw_fields
secondary_claim
Time: 2025.8 · Method: EO-1 · Institution: Shanghai AI Lab · Project: [![Website](https://img.shields.io/badge/Website-0A7DBD)](https://eo-robotics.ai/eo-1) · Model: VLA · Data: ![Real][data-real] ![General][data-general]
#table-embodied-foundation-models-vla-wam, table 17, row 28
model.reported_release_time
secondary_claim
2025.8
#table-embodied-foundation-models-vla-wam, table 17, row 28
model.type
secondary_claim
VLA
#table-embodied-foundation-models-vla-wam, table 17, row 28
model.training_data_layers
secondary_claim
real_robot · general
#table-embodied-foundation-models-vla-wam, table 17, row 28
model.institution
secondary_claim
Shanghai AI Lab
#table-embodied-foundation-models-vla-wam, table 17, row 28
catalog.curation
secondary_claim
tier: editorial_focus · collection: WorldBench Awesome Embodied Data Pyramid · policy: human_curated_priority
#table-embodied-foundation-models-vla-wam, table 17, row 28

Additional references

OpenBot notes

  • Paired with EO-Data1.5M and EO-Bench; keep training and benchmark relations explicit.

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