modules
Embodied Intelligence and Learning
Ground model output in physical evidence, safety limits and accountable review before it can influence machines.
A model proposes a new grasp after repeated failures. MAXACOR checks the object, tooling, confidence, safety envelope and validation record before the proposal can leave the lab or enter a live workflow. Prometheus owns model training and release; MAXACOR owns embodied grounding, validation and physical-use admission.
When it matters
A model proposes a new grasp after repeated failures. MAXACOR checks the object, tooling, confidence, safety envelope and validation record before the proposal can leave the lab or enter a live workflow.
data Viz
data View
section: module-scene
Section 1
col Title: VLM and VLA Grounding
col Cont: A model describes a valid action for the wrong end effector. The mismatch is caught before the proposal reaches execution.
Section 2
col Title: Active Learning Handoff
col Cont: A false rejection is corrected by quality and packaged with the original data, model and review outcome. Training receives useful evidence without silently changing the live model.
Section 3
col Title: Model Release Safety
col Cont: A new release improves average accuracy but fails on one safety-critical class. Deployment can be denied or limited before the weakness reaches the floor.
Embodied Intelligence and Learning
data View: maxacor-embodied-intelligence-learning:submodules
VLM and VLA Grounding
desc: Tie language, vision and action proposals to current objects, geometry, equipment and safety context.
link: /sub-modules/maxacor-vlm-vla-grounding
Active Learning Handoff
desc: Turn reviewed errors and edge cases into governed learning evidence.
link: /sub-modules/maxacor-active-learning
Model Release Safety
desc: Check model versions, evaluation evidence and rollback conditions before embodied use.
link: /sub-modules/maxacor-model-release-safety
VLM and VLA Grounding
action: Tie language, vision and action proposals to current objects, geometry, equipment and safety context.
mechanism: A model describes a valid action for the wrong end effector.
workflow role: A model describes a valid action for the wrong end effector.
operational outcome: The mismatch is caught before the proposal reaches execution.
maturity: specified
evidence: MAXACOR Part 7, §§4–9.
Section 1
Operations team
Section 2
Engineering team
Section 1
Qualified physical state
Section 2
Current work context
Section 1
Evidence-bearing operational disposition
Section 1
Model fluency does not establish physical feasibility.
Active Learning Handoff
action: Turn reviewed errors and edge cases into governed learning evidence.
mechanism: A false rejection is corrected by quality and packaged with the original data, model and review outcome.
workflow role: A false rejection is corrected by quality and packaged with the original data, model and review outcome.
operational outcome: Training receives useful evidence without silently changing the live model.
maturity: specified
evidence: MAXACOR Part 7, §§4–9.
Section 1
Operations team
Section 2
Engineering team
Section 1
Qualified physical state
Section 2
Current work context
Section 1
Evidence-bearing operational disposition
Section 1
MAXACOR does not train or promote models.
Model Release Safety
action: Check model versions, evaluation evidence and rollback conditions before embodied use.
mechanism: A new release improves average accuracy but fails on one safety-critical class.
workflow role: A new release improves average accuracy but fails on one safety-critical class.
operational outcome: Deployment can be denied or limited before the weakness reaches the floor.
maturity: specified
evidence: MAXACOR Part 7, §§4–9.
Section 1
Operations team
Section 2
Engineering team
Section 1
Qualified physical state
Section 2
Current work context
Section 1
Evidence-bearing operational disposition
Section 1
Prometheus release approval is necessary but not sufficient for physical use.