ChronoForest boosts robot route planning by 34.5% with multi-tree diffusion

ChronoForest tackles a core challenge in offline robot navigation: how to plan long routes when only short-horizon trajectory data is available. The key innovation is a closed-loop system that combines an anchor-chaining tree diffusion planner (for local bridge search) with an online multi-tree orchestrator (for global route re-solving). The planner uses temporal distance for short-range guidance and node evaluation, while the orchestrator validates long-range anchor connectivity via bridge evidence and repeatedly re-solves the route to correct poor temporal orderings. This dual-macro/micro approach avoids the trade-off between search cost and path quality that plagues naive composition of short segments.

The results are striking. On OGBench AntMaze-Stitch, ChronoForest achieves 99.8% success on medium, 99.3% on large, and 99.5% on giant splits—an improvement of up to 34.5 percentage points over previous diffusion-based planners. On Hamiltonian route-composition benchmarks, the online re-solving mechanism substantially improves route quality while remaining far cheaper than exhaustive planning. The paper provides extensive ablations (40 pages, 7 tables) showing the method's robustness across varying horizon lengths and waypoint counts. For robotics researchers and practitioners, ChronoForest demonstrates that efficient long-horizon planning is feasible even with limited offline data, potentially accelerating applications in warehouse automation, autonomous exploration, and multi-agent coordination.