Add independent physical objective evaluation

[yuecideng]

Description

Stack

• Layer: 2/2
• Base: codex/task-catalog
• Depends on: Add shared task catalog and static gallery #664
• Followed by: none

This PR now contains only the physical-objective layer originally developed with the task catalog. The catalog, CLI, static gallery, packaging foundation, and their focused tests have moved to #664.

• Add an opt-in ordered stable-region objective with per-environment progress, sustained-duration requirements, revocable final success, isolated snapshots, and partial reset.
• Keep physical results separate from program completion, segment acceptance, termination, and dataset persistence. Stable pose and velocity do not establish gripper release.
• Add explicit expert-controller trajectory replay with joint-layout and cadence validation while preserving legacy raw-policy replay.
• Add an expert/dynamic-replay evaluation runner with bounded initial XY variation, effective seeds, actual initial poses, configuration/component snapshots, and separate execution, physical, acceptance, and persistence outcomes.
• Add the repeated_pick_place objective deployment, checked-in startup evidence, public API documentation, project context, and focused tests.

Refs #106 and #655. This remains the first incremental physical-evaluation slice; learned-policy adapters, named multi-run experiments, and broader predicates remain follow-up work.

Dependencies: #664 must merge first. No new Python dependencies are introduced. Real physical qualification requires a DexSim build compatible with the repository spawn descriptor API.

Validation

• black . with Black 26.3.1: no files changed.
• python docs/scripts/check_api_docs.py: 2186/2186 exports documented.
• python .agents/skills/project-dev-context/scripts/context.py check: passed.
• python -m compileall -q embodichain embodichain_tasks: passed.
• Architecture snapshot generation and both architecture validators: passed (113 nodes, 227 edges).
• Focused objective/replay/evaluator pytest collection is blocked in the current local runtime by missing dexsim.engine; no functional assertions ran locally after the split.
• Before the history-only split, the same feature tree reported 123 passing focused tests. Fresh stacked-branch CI is authoritative.

The checked-in startup record reports an initialization error and unavailable physical outcome because the available DexSim build rejected com_quaternion. It does not claim either measured failure or success.

Type of change

• New feature (non-breaking change which adds functionality)
• Documentation update

Screenshots

Not applicable. The dependent catalog PR provides the static gallery UI.

Checklist

• I have run the black . command to format the code base.
• I reviewed affected documentation and agent context, updated it where needed, or explained why no update was needed.
• Public API changes are reflected in the API docs (python docs/scripts/check_api_docs.py).
• I have added tests that prove the feature works at the covered boundaries; local runtime limitations are documented above.
• Dependencies have been reviewed; no dependency changes are required.

[greptile-apps]

Confidence Score: 5/5

The PR appears safe to merge; no actionable regressions or repository-rule violations remain.

Summary

Adds shared task catalog discovery and gallery export, plus independent physical-objective evaluation and expert/dynamic-replay qualification.

• Introduces catalog-backed list-task and show-task behavior while preserving uncataloged tasks.
• Adds ordered stable-region objectives with isolated per-environment lifecycle state.
• Adds evaluation and controller-replay support with separate execution, physical, acceptance, and persistence outcomes.
• Packages task metadata, objective examples, validation records, documentation, and focused tests.

Diagram

%%{init: {'theme': 'neutral'}}%%
flowchart LR
    C[Task catalog YAML] --> D[Shared catalog model]
    D --> L[list-task and filters]
    D --> S[show-task]
    D --> G[Static HTML gallery]
    Y[Deployment configuration] --> E[EmbodiedEnv]
    O[Objective component] --> E
    E --> X[Expert execution]
    X --> R[Dynamic controller replay]
    E --> P[Physical-objective snapshots]
    X --> A[Acceptance outcome]
    X --> T[Persistence outcome]
    R --> P
    P -. remains independent .-> A
    P -. remains independent .-> T

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