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Two-robot AUTO study6 min read

Four HIVE TIPs in 30 seconds

Research note Uncalibrated simulation

A two-robot scripted planner completed four HIVE TIPs before the end of a 30-second AUTO fixture. Its cleanest witness, Red seed 31260975, finishes at 2.542, 10.200, 18.858 and 29.317 seconds. All four are completion events strictly before the cutoff. [1]

This is an alliance result with two active robots, not a solo record. The four-TIP sequence appeared in two of 48 deliberately selected seeds; none of the 12 unselected paired seeds reached four. We report both results because finding a possible sequence and demonstrating reliability are different research questions. [1][2]

29.317 sfourth TIP in the clean witness
2 / 48four-TIP witnesses in selected seeds
0 / 12four-TIP outcomes in unselected control seeds
Recorded match // Seed 31260975

Watch the four-TIP AUTO opening

The first 30 seconds of the separately recorded full-match continuation, seed 31260975. Red completes four AUTO TIPs; choose Full match to watch the continuation to 12 total TIPs and a 256-211 finish. This recording is not the independent discovery fixture.

Loading the recorded match...

Red alliance
-- points[*]
-- TIPs
Blue alliance
-- points[*]
-- TIPs

[*] Starting score: Each alliance begins with 10 modeled field-content points: 4 GARDEN points (four starting POLLEN at 1 point each) plus 6 HIVE-retained points (three starting NECTAR at 2 points each). These are already present at setup, not earned by robot actions. We keep the original recorded totals; contents points change as pieces move and are not a fixed 10-point bonus. Replay evidence.

Red robots / NECTARBlue robots / NECTARPOLLENDotted border: LOADING ZONE
0.000 sFrame --

Starts paused at 1x. Positions are original recorded frames, not interpolated or resimulated. Exact TIP event timestamps can fall between frames; event jumps show the first recorded frame at or after completion.

Playback provenance

Original recording information appears when playback loads.

Download the sanitized replay data

Source [3]. Uncalibrated simulation; no claim of physical robot performance.

More useful work on the same twelve seeds

Mean Red AUTO TIPs in separate, unselected paired seeds 41270001-41270012. The scripted baseline and staged planner use the same physical model. The planner improves the mean without reaching four in this sample.

Scripted baseline2.083
Staged planner2.917
Scale starts at zero; values are mean Red AUTO TIPs. Source [2].

Parallel collection beats a serialized handoff

The lower-wall robot shoots its four preloads at the initial HIVE face. Its opposite-wall partner waits for the first TIP, then interleaves its own preload shots with extraction from the nearby FLOWER, freeing inventory space as it works. Meanwhile, the first robot gathers the lower-wall GARDEN POLLEN for the next volley. [1]

The second robot continues to another FLOWER supply. Both robots gather and shoot conserved loose POLLEN and released NECTAR; FLOWER scoring itself stays disabled. The fourth TIP is not explained by four fresh GARDEN balls alone: recycling and physically picked-up released pieces contribute to it. [1]

This experiment selects legal observed actions in unchanged v10 physics. It is an offline scripted controller, not an RLE-trained or promoted MAPPO actor. The model's 50% open-field AUTO pickup rejection remains enabled. [1]

Keep the selection bias next to the headline

The discovery set was selected because archived recordings had already shown three Red AUTO TIPs on those seeds. The staged controller reached four on seeds 31260975 and 31261165. The second witness completed at 29.575 seconds but required modeled teammate-separation retreats; the first had no such retreats in its 30-second fixture. [1]

On the twelve unselected paired seeds, mean Red AUTO TIPs rose from 2.083 to 2.917: ten improvements, two ties and no regressions. Both arms had zero PIN and teammate-overrun flags. This is a small, encouraging model comparison, not a general four-TIP success rate or a claimed confidence interval. [2]

A fast opening does not settle the full match

A separately recorded continuation of seed 31260975 included one AUTO PARK and one late TELEOP PARK. Red finished with 12 total TIPs and 256 points versus Blue's 211. That continuation must not be confused with the separate 13-TIP witnesses in the full-match study. [3]

The useful design hypothesis is to pipeline collection and scoring around the changing HIVE face instead of making both robots wait on one serial workflow. Before calling it a competition strategy, repeat it across broader held-out conditions and physically measure intake reliability, travel, mechanism timing and recovery after a missed first volley. [1][2]

Four completions, one strict cutoff

Red seed 31260975, selected-seed scripted fixture. TIP 4 completes about 0.683 seconds before 30.000. A TIP merely started before 30 would not count.

Four completions, one strict cutoffCumulative completed TIPs over 30 seconds. Completions: 2.542, 10.200, 18.858, 29.317 seconds. Exact values are available in the event table below.0s7.5s15s22.5s30s04Completed TIPs
Cumulative events, not motion interpolation. Source [1].
Exact TIP completion times
TIPCompleted atPhase
12.542 sAUTO
210.200 sAUTO
318.858 sAUTO
429.317 sAUTO

Discovery and evaluation are different samples

The selected set can establish a modeled possibility. The unselected paired comparison is the more relevant evidence about improvement.

Discovery and evaluation are different samples
SampleTrialsFour-TIP outcomesWhat it supports
Previously three-TIP replay seeds482A best-case sequence exists in this model
Unselected paired Red seeds12 per arm0 in either armHigher mean AUTO count in this small comparison

Source [2].

Evidence boundary

What this study does not prove

  • Two of 48 is conditional on favorable replay selection, not a general success probability.
  • Two active allied robots contribute to this opening; do not compare it as a solo AUTO result.
  • The model uses idealized sensors, 2% conditional misses, estimated 0.8-second HIVE movement and uncalibrated pickup behavior.
  • The pure AUTO fixture and its full-match continuation are separate runs with different end-of-match behavior.
  • Four is the best witnessed count in these experiments, not a proven maximum or hardware demonstration.
BloxBuzz and the RLE

Where to play BIOBUZZ

BloxBuzz is where you can play BIOBUZZ; the RLE is where our agents train and are evaluated. The RLE does not run in Roblox, and our goal is for it to use the same game engine, rules and physics as BloxBuzz. These results come from the RLE, not from BloxBuzz gameplay, and this study does not verify that the two match.

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Sources and evidence

Linked JSON files are sanitized research extracts with sample counts, event witnesses and source hashes. Embedded 2D playback uses exact retained frame coordinates and TIP events; the full original actor logs remain privately retained. These extracts are not a complete reproduction package.

  1. Selected-seed four-TIP experiment

    The 48-trial discovery extract preserves both four-TIP event sequences, selection method and SHA-256 of the original experiment file.

  2. Unselected paired twelve-seed comparison

    Per-seed baseline and staged Red counts, safety indicators, means, ten improvements and two ties. Kept separate from the selected discovery set.

  3. Full-match continuation of seed 31260975

    Separately recorded 150-powered-second run: Red 12 TIPs, scores 256-211. The extract includes its source-file hash.

  4. Official BIOBUZZ Competition Manual

    Consult the current rules before adopting a starting pose, collection plan or parking objective.

Download the evidence extract

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  1. Full-match study

    How many HIVE TIPs are possible?

    Our full-match RLE study reached 13 simulated HIVE TIPs per alliance. Here is the evidence, the distribution, and why that is not a proven maximum.

    Read the study
  2. Solo AUTO study

    Three HIVE TIPs. One autonomous robot.

    The solo AUTO RLE study reached three simulated TIPs with an inactive teammate. We separate the rare training frontier from frozen-policy performance.

    Read the study
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    Inside the evidence

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Research studies use a Reinforcement Learning Environment (RLE). Modeled outcomes need physical validation.