{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "abc12345anouys-evobot--watch-one-libero-task-learn-over-attempts",
 "repo": "ABC12345anouys/EvoBot",
 "repo_url": "https://github.com/ABC12345anouys/EvoBot",
 "repo_slug": "abc12345anouys-evobot",
 "title": "Watch one LIBERO task learn across retry attempts",
 "page_title": "How to watch one LIBERO task learn across retry attempts with Evo Bot",
 "summary": "Run a single LIBERO task with a persistent simulator and see how failures change the next attempt.",
 "who": "A researcher studying how a skill-based agent recovers from failure, who wants to inspect one task closely.",
 "problem": "Reading summary scores hides how the agent adjusts parameters, grasp positions or methods after each failed attempt.",
 "outcome": "A run on one task, such as libero_spatial:0, that logs each attempt, stops at the step cap and keeps the video unless disabled.",
 "steps": [
  "Set up the environment and PYTHONPATH as in the README quick start",
  "Run `PYTHONPATH=$PWD:/path/to/LIBERO MUJOCO_GL=egl python -u scripts/ipc_learn.py libero_spatial:0 --max-attempts 30 --sock /tmp/libero.sock` (inferred, check the README)",
  "Watch the attempt output and note the failure reason the agent reports for each failed try",
  "Check the progress file logs/libero_agent_progress.json for the recorded result of the task"
 ],
 "agent_prompt": "In the EvoBot repo at <repo path>, run the task libero_spatial:0 with scripts/ipc_learn.py and --max-attempts 10. Report how many attempts were used, whether the task passed, and the failure reasons you saw. Verify that the progress entry for libero_spatial:0 matches the outcome you report.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The step cap defaults to 7000 simulation steps and the wall-clock timeout only triggers when the program stalls, so a single attempt can run for a long time on a slow machine.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/abc12345anouys-evobot/watch-one-libero-task-learn-over-attempts/",
 "md": "https://dedupedai.com/use-cases/abc12345anouys-evobot/watch-one-libero-task-learn-over-attempts/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/abc12345anouys-evobot--watch-one-libero-task-learn-over-attempts.json",
 "api_version": "v1"
}