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How to reproduce a BEHAVIOR task mean Q-score with Robo Harness

Run the five fixed BEHAVIOR instances for one task and compare the mean with the reference value.

  • Top pick
  • Research
  • Advanced
  • 8 hours
  • From the README
  • Any agent

Who it is forA robotics researcher checking a published embodied-agent result on their own GPU machine before building on it.

The problemPublished numbers for LLM-driven manipulation are hard to trust without rerunning the same instances under the same protocol.

The outcomeA validation report in .local/reports/ that shows whether the task mean matches the reference within the stated tolerance.

What you need first

  • Linux x86-64 machine with an NVIDIA RTX GPU and the CUDA 12.4 toolkit (the validation host had 48 GB VRAM per GPU)
  • Python 3.11 and Claude Code 2.1.259 for the paper configuration
  • Licensed BEHAVIOR scene data, plus the Isaac Sim terms that the README says apply
  • A separately running model server with an Anthropic-compatible /v1/messages endpoint that supports image input, tool calls and streaming
  • Enough local disk for the BEHAVIOR data and the Hugging Face assets

Steps

  1. git clone --recurse-submodules https://github.com/BinceQu/RoboHarness.git
  2. ./scripts/setup.sh
  3. cp configs/example.json configs/local.json
  4. ./run.sh --list
  5. ./scripts/reproduce_task.sh task01 --gpu 0 --dry-run
  6. ./scripts/reproduce_task.sh task01 --gpu 0 --run-dir runs/task01-001
  7. python3 scripts/report_validation.py runs/task01-001 --output .local/reports/task01-001 --check-live --require-match (inferred, check the README)

Prompt for your agent

In the RoboHarness repo, run the task <task id from ./run.sh --list> with --dry-run first, then the full five-instance run into a new run directory, then generate the validation report. Report the task mean from runs/<run>/summary.json and whether --require-match passed. Verify that all five instance IDs are 301, 304, 306, 308 and 310 before you start.

Caveat. The full run needs licensed BEHAVIOR data and a separately served model, and the README gives 48 GB VRAM per GPU only as the tested capacity, not a minimum.

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