BreakAI

League of Robot Runners 2026: Rovnou Receives Combined Track Top 10 Award (6th Place)

July 28, 2026 · Updated September 29, 2026

BreakAI’s robot traffic-control project, Rovnou, entered the 2026 League of Robot Runners, a large-scale multi-robot coordination competition sponsored by Amazon Robotics. The organizers awarded Rovnou the Top 10 Award (6th Place) — Combined Track in the Main Round. Kuriyama Daisuke was the team’s sole listed member.

The organizers published their Main Round final-results announcement in September. The exact award and placement were confirmed in their September 29 award email. Earlier, at the July 22 submission deadline, the leaderboard showed Rovnou 8th among 68 displayed entries in the Combined Track, with a score of 5.55 and 134,905 completed tasks. The score and task total below describe that historical snapshot.

July deadline standing and final award

In an email to participants dated July 24, 2026, the organizers reported 3,361 submissions from 69 teams. The table below records the deadline leaderboard; Rovnou’s final Combined Track award placement is 6th.

Leaderboard at the submission deadline

RankTeamScore
1No Man’s Sky10.629
2SmartPath9.639
3Trzy Kwaterki7.034
8Rovnou5.55

Rovnou’s score comprised the following instance-level results:

InstanceTasks completedScore
bos6,8680.433
fulfill-A15,7260.576
fulfill-B32,0650.438
fulfill-C10,0820.453
iron57,0450.423
maze-A1270.605
maze-B2080.813
orz4,8950.229
rand-A6,6420.330
room-A5800.591
room-B6670.658
Total134,9055.55

Among the 11 instances, maze-B produced Rovnou’s highest relative score at 0.813, while iron contributed the most completed tasks at 57,045. Because map geometry, robot density, and delay conditions varied substantially between instances, a single universal configuration was not enough.

What the competition tested

The League of Robot Runners models continuous robot work in settings such as warehouses and factories. The 2026 competition added stochastic action delays to the existing task-scheduling and path-planning challenge.

The Main Round ran from April 14 to July 22, 2026 (Anywhere on Earth, UTC−12). In the Combined Track, each entrant implemented all three layers:

  1. Task scheduling: deciding which robot should receive each task
  2. Path planning: producing collision-free routes for the fleet
  3. Execution policy: safely executing those plans when unexpected delays occur

This was a lifelong multi-agent path-finding problem, not a one-shot goal-reaching benchmark. Results depended on sustained throughput as well as planning quality, replanning latency, scheduler behaviour, runtime congestion, and memory use.

What Rovnou built during the competition

Rovnou’s development focused on measuring the bottleneck of each map and delay condition rather than applying one setting everywhere.

Map-specific execution

Robot density, corridor width, turning cost, and available compute time differ by map. We built policy dispatch that can select the planner, scheduler, agent cap, and compute budget for each problem class.

Route guidance and distance estimates

We added orientation-aware distance estimates, traffic-flow guidance, and diagnostics for intersections and narrow corridors. These tools make it possible to detect cases where individually short routes create fleet-wide congestion.

Task assignment

We tested candidate filtering, distance-table reuse, and parallel computation instead of exhaustively evaluating every task on every cycle. The experiments reinforced that choosing the next job can matter as much as planning the route to it.

Detecting and recovering from blocked doorways

Room-style maps can stall when several robots converge on a single entrance. We added diagnostics for waits, dependency chains, and doorway blockers, together with limited recovery mechanisms.

Reproducible measurement

We developed PlanViz inspection, log analysis, same-binary A/B and ABBA comparisons, build-and-run provenance checks, and regression tests. These controls separate increased mechanism activity or CPU usage from genuine improvement in completed-task throughput.

Not every implemented experiment improved the leaderboard result. Some changes won locally but did not reproduce in the evaluation environment; others increased CPU use without increasing throughput. An important outcome of the competition was therefore the measurement infrastructure needed to reject ineffective hypotheses, not only the mechanisms that were retained.

How the leading team implemented its solution

The supplied 26-page document, No Man’s Sky team — League of Robot Runners 2026 solution, describes the architecture, planner, scheduler, map-specific configuration, and 87-submission development history of the first-place team.

1. A lightweight main-thread dispatcher

The main thread, called once per simulator tick, avoids heavy search. It collects state, launches asynchronous work, receives completed results, and commits plans. Task assignment runs in a background scheduler thread, while path planning runs in an asynchronous planner worker.

2. A World model that predicts the next execution boundary

The solution maintains a World model containing in-flight primitives, delays, already-issued action queues, and task progress. Planning starts from a predicted future boundary rather than always using the immediately observed state.

It selects one of three modes by map:

ModeBehaviourMain applications
SYNC_STEPPlans synchronously from the actual state; no prediction error but less compute timemaze, room
STEPPredicts the next boundary and computes asynchronously between ticksfulfill-A/B, iron, orz, bos, rand-A
LEAD_STEPLaunches just before the boundary to preserve a fresher snapshotfulfill-C

3. EPIBTX followed by parallel LNS/ALNS

The initial solution is built with EPIBTX, an extension of PIBT-style priority inheritance and backtracking. Multiple Large Neighborhood Search workers then repair local waits and congestion. Initial construction stays single-threaded to preserve quality; the improvement phase is parallel.

4. Shared exact distances and traffic rules

For each robot, the scheduler considers the nearest 100 tasks and combines a sparse Hungarian assignment with greedy matching. Planning uses a Guidance Graph, PriorityField, HeuristicMatrix, and DynamicHeuristicMatrix so that lane direction, intersections, and congestion influence its distance estimates.

5. Map-specific mechanisms

The document reports its largest gains as +152% from a lane Guidance Graph on orz, +91% from LEAD_STEP on fulfill-C, and +66% from exact GoalRows distances on iron. Its conclusion is that selecting the appropriate planning mode and heuristic for a map-specific bottleneck produced larger gains than tuning one global set of coefficients.

The document also reports that roughly one-third of the team’s 87 submissions were regressions or isolation experiments. The broader lesson is not merely a list of algorithms: change one thing at a time, measure it, and make adoption decisions per map.

We have not speculated about SmartPath’s or Trzy Kwaterki’s implementation because the materials reviewed for this article do not provide evidence for their approaches.

What the result means for Rovnou

The competition result demonstrates an integrated test of task assignment, path planning, and delay-aware execution across 11 large simulated scenarios. It does not, by itself, prove real-warehouse safety, communications reliability, integration with an existing fleet-management system, or maintainability.

Rovnou will carry the competition’s lessons—map-specific optimisation, predictive versus synchronous execution, and reproducible measurement—into traffic-control software that can be validated in real deployments.

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