Development notes
This page keeps active, incompletely validated work separate from the package's release-facing claims. The features below may be useful for research and API review, but they are not validated simulator or robot capabilities.
Experimental feature status
| Work in progress | Evidence currently available | Evidence still required |
|---|---|---|
| Skill capsules and demonstration ingestion | Contract, serialization, and round-trip tests | Dataset-scale curation and retrieval study |
| Primitive programs and recovery graphs | Unit tests and a deterministic synthetic state-machine example | Closed-loop simulation and real-robot task trials |
| Analytic and learned candidate portfolios | Isolated candidate and common-gate tests | Matched robot-feasibility and task-outcome evaluation |
| Persistent relational FATROP retargeting | Solver tests and exploratory geometric fixtures | Frozen confirmatory protocol with full orientation and robot constraints |
| Gripper and event-based segmentation | Sample-exact and hysteretic change-point tests | Annotated demonstrations and semantic boundary evaluation |
See the primitive-program guide, provisional API inventory, and experimental validation notes for implementation details and reproducible component checks.
VLA evaluation hypothesis
Initial-frame relative pose chunks are already invariant under one shared world-frame transform. DHB-XR's research hypothesis is not that DHB is more frame-invariant. It is that a structured motion representation may provide a useful common interface for retargeting, retrieval, time normalization, tokenization, and later execution stages.
The release currently verifies numerical frame stability and software interfaces. A matched learned-policy study is still required before claiming that a DHB action head improves VLA task success, generalization, token rate, or data efficiency over an initial-frame relative-pose head.
Promotion rule
Move a development item into release highlights only when its claim is backed at the corresponding evidence level:
- Unit tests establish software behavior.
- Isolated examples establish bounded component behavior.
- Frozen matched benchmarks can support representation comparisons.
- Closed-loop simulation can support simulator-task claims.
- Repeated real-robot trials are required for physical-task claims.
Results at one level must not be described as evidence for a higher level.