RFC: Feedback abstraction for adaptive/closed-loop sanitation research
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python
- Domain
- backend-api-design
Research direction
Start by reviewing the existing Solution generation and Run recording workflow, including the constraint-checking, approval, and hardware-execution pathways. Then compare those boundaries with the proposed Observation and FeedbackPolicy concepts. Done means maintainers have agreed whether this abstraction belongs in openlifu-python, what related functionality exists, and where planning, observations, and hardware control should meet.
Written by the indexing model from the issue text.
Description
OpenLIFU currently provides a pipeline form treatment planning through Solution generation, hardware execution, and Run recording. Would there be interest in defining a lightweight abstraction for research workflows where observations from a completed simulation or sanitation can inform generation of a subsequent solution?
An initial implementation could remain entirely simulation/offline-oriented and make no assumptions about the measurement modality or automatic hardware execution.
Conceptually:
Protocol -> Solution -> execution/simulation -> Observation -> FeedbackPolicy -> next Solution
Potential research uses could include focal correction, aberration correction, adaptive apodization, pressure/intensity targeting, or uncertainty-guided sonication planning.
One possible design would define an Observation representation plus a policy/callback interface that proposes modifications while leaving existing constraint checking, approval, and hardware execution pathways intact.
Before working on an implementation, I'd be interested in feedback on whether this abstraction belongs in openlifu-python, whether related functionality already exists/planned, and what boundary maintainers would prefer between planning, observations, and hardware control.
- Dominant language
- Python
- Stars
- 29
- Forks
- 21
- Avg merge
- 19h 51m
- Merged PRs (30d)
- 5
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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