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evosearch

Run the CaP Fix Loop + Evolutionary Search experiment on a user-supplied list of low-performing benchmark tasks: iterative K=8 candidate search on development seeds 51–65, validation selection, and final held-out evaluation on seeds 1–50.

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NVlabs/ASPIRE星标 887(GitHub 887 + cccskills 0)

实测评分 未实测

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包含的文件(8)

  • SKILL.md3.1 KB
  • clean-task-slate.md981 B
  • main-agent-prompt.md5.9 KB
  • scripts/analyze_evosearch_traces.py9.1 KB
  • scripts/evosearch_eval.py13.1 KB
  • skills/evosearch-iteration.md26.9 KB
  • skills/README.md360 B
  • subagent-prompt.md16.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Fix Loop + Evolutionary Search

Use this skill for intensive debugging of difficult tasks — Evolutionary Search's multi-candidate search finds strategies the single-pass fix loop misses. It builds on the iterative-debugging skill: each task's fix loop fix_code.py (when present) is the baseline to beat, seeded verbatim as candidate_A. LIBERO-Pro runs use --init-mode seeded so development seeds 51–65 and held-out seeds 1–50 are distinct scenes.

Setup

  1. Complete benchmark setup for the target embodiment (docs/libero-pro.md, or docs/robosuite.md), including the pinned environment (for LIBERO: scripts/bootstrap_libero.sh → .venv-libero) and SAM3 authentication.
  2. Export the benchmark environment variables (for LIBERO: LIBERO_CONFIG_PATH="$PWD/.libero", MUJOCO_GL=egl, PYOPENGL_PLATFORM=egl, EGL_PLATFORM=device, TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1).
  3. Start perception services (scripts/supervise_services.sh) and a persistent coordinator session.
  4. Confirm the Fix Loop experiment has produced fix_code.py (and a baseline held-out rate) for the target tasks when available.
  5. Read docs/api-reference.md, docs/run-artifacts.md, and main-agent-prompt.md.

Run Order

  1. Take the task list from the user (tasks below the success threshold; any set of (benchmark, suite, task_id) cases) and follow main-agent-prompt.md as the coordinator.
  2. Fill subagent-prompt.md once per task and dispatch subagents on the available worker GPUs.
  3. Subagents read the experiment-specific companion skills under skills/ when generating candidates, iterate with scripts/evosearch_eval.py, and run the Stage 2 held-out evaluation themselves with the shared .claude/skills/iterative-debugging/scripts/run_validation.py before returning.
  4. Use clean-task-slate.md before rerunning a task or suite.

Files

FilePurpose
main-agent-prompt.mdCoordinator runbook for task dispatch and result collection
subagent-prompt.mdPer-task Evolutionary Search iteration prompt
clean-task-slate.mdReset checklist before reruns
skills/evosearch-iteration.mdCandidate/eval/keyframe iteration mechanics
scripts/evosearch_eval.pyCandidate × seed matrix evaluator
scripts/analyze_evosearch_traces.pyPost-eval per-candidate signal analysis
docs/api-reference.mdPublic CaP program API reference
../iterative-debugging/skills/Shared robot skills (localize/grasp/transport/manipulation)

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中文简介正在准备中。当前显示原文说明。

NVlabs/ASPIRE8872026年10月16日 更新

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中文简介正在准备中。当前显示原文说明。

NVlabs/ASPIRE8872026年10月16日 更新

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中文简介正在准备中。当前显示原文说明。

NVlabs/ASPIRE8872026年10月16日 更新

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中文简介正在准备中。当前显示原文说明。

NVlabs/ASPIRE8872026年10月16日 更新

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