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circle-great
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behavior-1k-2026-partial20-submission

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task
stringclasses
20 values
instance_id
int64
301
310
rollout_id
int64
0
0
steps
int64
2.4k
14.2k
success
bool
2 classes
agent_distance
dict
normalized_agent_distance
dict
q_score
dict
time
dict
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cook_bacon
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cook_bacon
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cook_bacon
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cook_bacon
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cook_bacon
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cook_hot_dogs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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hiding_Easter_eggs
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End of preview. Expand in Data Studio

BEHAVIOR-1K 2026 Pi0.5 Partial-20 Self-Evaluation

This repository contains an unedited partial self-evaluation for the 2026 BEHAVIOR Challenge.

Scope

  • BEHAVIOR-1K version: v3.9.1
  • Policy: Pi0.5 task-embedding policy derived from IliaLarchenko/behavior-1k-solution
  • Robot: bundled R1Pro configuration
  • Evaluation wrapper: omnigibson.eval.wrappers.DefaultWrapper
  • Public instance indices: 0-9 (instance IDs 301-310)
  • Rollouts per instance: 1
  • Evaluated tasks: 20 / 100
  • Submitted rollout pairs: 200 / 1000
  • Missing rollouts: 800, which should be scored as zero under the partial-submission rule

The evaluated task indices are:

0, 1, 6, 8, 10, 12, 17, 22, 30, 31, 32, 34, 35, 37, 38, 39, 40, 42, 45, 46

Artifacts

  • behavior-2026-self-eval-results.zip: 200 original evaluator JSON files.
  • <task>/videos/*.mp4: 200 corresponding original rollout videos.
  • summary.json and summary.csv: local summaries for inspection.
  • verification.json: structural verification report.

The JSON and MP4 outputs have not been edited. Each submitted JSON has one same-stem MP4. Local verification reported 200 JSON files, 200 videos, 200 paired outputs, no duplicates, no incomplete artifacts, and successful ffprobe decoding.

Results summary

  • Full task successes: 10 / 200 rollouts.
  • Mean Q-score over the 20 evaluated tasks: 0.1184305556.
  • Mean Q-score after assigning zero to the 80 missing tasks: approximately 0.0236861111, subject to organizer verification.

This is explicitly a partial submission, not a complete 100-task public self-evaluation. No missing task is claimed as evaluated.

Evaluation protocol

Each reported public instance was evaluated once with the evaluator's default task-specific timeout and --write-video. The policy uses onboard RGB and proprioception and does not consume ground-truth segmentation, object state, target pose, global robot pose, or other simulator-only privileged inputs.

Public policy image and evaluation setup

  • Image: ghcr.io/sqdy2669/behavior1k-pi05:2026-final
  • Digest: sha256:e54e1f739016db880263a7480fca80ce5f686c746737bb46d5b74afa0b55b03c
  • Platform: Linux AMD64 with one NVIDIA GPU
  • Container port: 8000
  • Measured steady policy VRAM: approximately 7 GiB on an NVIDIA A800
  • Inference-time internet or external API dependency: none
  • Registry authentication: none; the image is publicly pullable

Start the policy server with the immutable digest:

docker pull ghcr.io/sqdy2669/behavior1k-pi05@sha256:e54e1f739016db880263a7480fca80ce5f686c746737bb46d5b74afa0b55b03c
docker run --rm \
  --gpus '"device=0"' \
  --ipc=host \
  -p 8000:8000 \
  ghcr.io/sqdy2669/behavior1k-pi05@sha256:e54e1f739016db880263a7480fca80ce5f686c746737bb46d5b74afa0b55b03c
curl --fail http://127.0.0.1:8000/healthz

Connect the official OmniGibson evaluator to ws://127.0.0.1:8000, using the bundled R1Pro configuration and omnigibson.eval.wrappers.DefaultWrapper. For example:

python -m omnigibson.eval.eval \
  --task-name <TASK_NAME> \
  --host 127.0.0.1 \
  --port 8000 \
  --instance-indices 0 1 2 3 4 5 6 7 8 9 \
  --num-rollouts 1 \
  --env-wrapper omnigibson.eval.wrappers.DefaultWrapper \
  --robot-config robot/r1pro.yaml \
  --output-dir outputs/<TASK_NAME> \
  --headless \
  --write-video

The exact wrapper, R1Pro configuration, and Docker packaging files are included in the final submission archive. A standalone training/reproduction source repository is not provided as an optional public release.
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