PPersenseAI

Project 01 · Temporal evaluation

Visual Reward Timeline

How do progress and failure signals evolve over time?

Synthetic Visual Reward Timeline showing representative frames and a rising dense reward curve

What the demo shows

An annotation-backed synthetic tabletop task aligns ten representative frames with dense reward, stage labels, terminal outcome, and an interpretable failure point. Success, missed-grasp, and object-drop scenarios show why a final score alone hides useful temporal structure.

Implementation approach

Deterministic scenario annotations drive a compact Python/Streamlit prototype. The public page contains presentation images only; private implementation source is not published here.

Public origins and related work

The research question draws on reward modeling, temporal credit assignment, and evaluation visualization. The prototype is an implementation study, not a claim to have originated dense reward or failure localization.

Limitations

Authored annotations, abstract synthetic video, and fixed scenarios. No learned visual model, calibrated probability, real-world evaluation, or operational claim.

Provenance statement

All depicted frames are independently generated geometric scenes. No real people, recordings, cases, confidential material, or external API appears in the demo.

Screenshot gallery

Successful synthetic task reward timeline
Successful completion: dense progress reaches terminal success.
Synthetic missed grasp reward timeline
Missed grasp: the failure point interrupts rising progress.

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Working on temporal evaluation, multimodal evidence, or interpretable reward signals?

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