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How to regenerate LIBERO task specs with deterministic ordering with Evo Bot

Rebuild subgoal orders for LIBERO tasks using only the automatic sort, with no model calls.

  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forA developer who wants reproducible subgoal orders for LIBERO tasks and has no DeepSeek API key.

The problemModel-generated task decompositions can differ between sessions, which makes debugging harder.

The outcomeTask specification YAML files under darwin/skills/configs/task_specs/ regenerated with the dependency-based order.

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. Activate the environment from the README setup
  2. Run python scripts/gen_task_specs.py --no-llm to regenerate all 30 tasks without calling a model
  3. Open one file in darwin/skills/configs/task_specs/ and check that the source field reads deterministic
  4. Run a single task to confirm the subgoal order runs without error, for example with python scripts/gen_task_specs.py --tasks libero_goal:7 --no-llm (inferred, check the README)

Prompt for your agent

In the EvoBot repo at <repo path>, run `python scripts/gen_task_specs.py --no-llm`. Report how many task spec files were written and confirm that each has source set to deterministic. Verify by opening one spec and comparing its subgoals list with the reference_order field.

Caveat. The README says the runtime does not call a model, but regenerating specs with the model needs DEEPSEEK_API_KEY and falls back to deterministic ordering if the key is missing.

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