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6 commits

Author SHA1 Message Date
Reuven
cf542ca29c style: apply cargo fmt formatting 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-12 20:57:18 -04:00
Claude
5051499619 feat: add RVF packaging module for robotics data 
New `rvf` feature flag enables the `ruvf::RoboticsRvf` wrapper that
bridges point clouds, scene graphs, trajectories, Gaussian splats, and
obstacles into the RuVector Format (.rvf) for persistence and similarity
search.

RoboticsRvf supports:
- pack_point_cloud (dim 3)
- pack_scene_objects / pack_scene_graph (dim 9)
- pack_trajectory (dim 3)
- pack_gaussians (dim 7) — converts PointCloud→GaussianSplatCloud→RVF
- pack_obstacles (dim 6)
- query_nearest (kNN via HNSW index)
- open/open_readonly/close lifecycle

9 unit tests covering create, ingest, query, reopen, dimension mismatch,
and empty data rejection. Also fixes unused import warnings in integration
tests. All 290 tests pass across default, domain-expansion, and rvf features.

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 14:08:30 +00:00
Claude
6916ed2c88 feat: add Gaussian splatting, motion planning, MCP executor, sensor fusion, and benchmarks 
New modules for ruvector-robotics:
- bridge/gaussian: GaussianSplat types, PointCloud→Gaussian conversion, vwm-viewer JSON export
- planning: A* pathfinding on OccupancyGrid with octile heuristic, potential field velocity commands
- mcp/executor: ToolExecutor dispatching ToolRequests to perception pipeline and spatial index
- perception/sensor_fusion: multi-sensor cloud fusion with timestamp alignment and voxel downsampling

Rewrites integration tests to use actual crate APIs instead of local reimplementations,
eliminating ~280 lines of false-positive test code. Adds 15 benchmark groups covering
all new modules (Gaussian conversion, A* planning, potential fields, sensor fusion, MCP execution).

All 270+ tests pass including domain-expansion feature.

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 13:23:53 +00:00
Claude
3f8b41b0a0 feat: optimize ruvector-robotics and integrate domain-expansion for cross-domain transfer 
Performance optimizations (net -134 lines):
- BinaryHeap kNN: O(n log k) vs O(n log n) full sort in SpatialIndex
- Zero-clone behavior tree tick via pointer-based borrow splitting
- VecDeque percept buffer for O(1) front eviction
- HashSet assigned_robots for O(1) membership checks
- Shared clustering module eliminates 3 duplicate implementations

Correctness fixes:
- UntilFail decorator 10k iteration guard prevents infinite loops
- OccupancyGrid bounds-checked get() returns Option<f32>
- Pipeline position_history capped at 1000 entries
- Skill learning gracefully handles empty demonstrations
- Anomaly type gets Serialize/Deserialize derives

Dead code removal:
- Remove unused TrajectoryPoint struct
- Remove unused tracing and rand dependencies

Domain expansion integration (behind `domain-expansion` feature flag):
- RoboticsDomain implements domain::Domain trait with 5 task categories:
  PointCloudClustering, ObstacleAvoidance, SceneGraphConstruction,
  SkillSequencing, SwarmFormation
- 64-dim embedding space compatible with planning/orchestration/synthesis
- Reference solutions, difficulty scaling, cross-domain transfer tests
- Enables Meta Thompson Sampling transfer between robotics and
  existing domains (Rust synthesis, structured planning, tool orchestration)

All 257 tests pass (231 unit + 25 integration + 1 doc-test).

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 05:22:32 +00:00
Claude
0cc25bf2bf chore: Remove unused imports from integration test 
https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 03:36:49 +00:00
Claude
85447c0bfc feat: Add unified ruvector-robotics crate with bridge, perception, cognitive, and MCP modules 
Consolidates robotics functionality into a single crate with four modules:
- bridge: Core types (Point3D, PointCloud, RobotState, Pose), spatial indexing,
  distance metrics, sensor converters, and perception pipeline
- perception: Scene graph construction, obstacle detection/classification,
  anomaly detection, trajectory prediction, and attention focusing
- cognitive: Behavior trees, perceive-think-act-learn loop, multi-criteria
  decision engine, three-tier memory system, skill learning from demonstration,
  swarm coordination with formations/consensus, and world model tracking
- mcp: Tool registry with 15 registered tools across 6 categories

Includes 26 passing tests (10 unit + 15 integration + 1 doc), 5 crate examples,
10 standalone binary examples, benchmarks covering 10 groups, and user guide.

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 03:35:54 +00:00