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How to resume the LIBERO agent runner and skip passed tasks with Evo Bot

Run the agent over all three LIBERO suites so that tasks already passed are skipped on each rerun.

  • Research
  • Intermediate
  • 2 hours
  • From the README
  • Any agent

Who it is forA researcher running a long batch of LIBERO tasks on a machine that may be interrupted.

The problemRestarting a full benchmark batch wastes time when many tasks already passed in an earlier session.

The outcomeA batch run that processes only unfinished tasks and records progress for each one.

What you need first

  • Python 3.10 in a conda environment (the README says Python 3.12 cannot run robosuite and its dependencies)
  • LIBERO source code, added to PYTHONPATH as described in the README
  • MuJoCo and robosuite (bundled with LIBERO)
  • MUJOCO_GL=egl on machines without a display
  • DEEPSEEK_API_KEY only if you want to regenerate task specs with the model

Steps

  1. Set up the environment and export PYTHONPATH=$PWD:/path/to/LIBERO and MUJOCO_GL=egl as in the README
  2. Run PYTHONPATH=$PWD:/path/to/LIBERO MUJOCO_GL=egl python -u -m darwin.agents.libero_runner --suites libero_spatial,libero_object,libero_goal (inferred, check the README)
  3. After the run, open logs/libero_agent_progress.json and count the tasks marked as passed
  4. Rerun the same command later to continue from the unfinished tasks

Prompt for your agent

In the EvoBot repo at <repo path>, run darwin.agents.libero_runner for the three suites with the PYTHONPATH and MUJOCO_GL settings from the README. Report how many tasks were skipped as already passed and how many were newly run. Verify the counts against the entries in logs/libero_agent_progress.json.

Caveat. The README says the progress file keys follow a pattern, but the exact skip logic beyond passed tasks is not described, so check the progress file yourself after each rerun.

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