[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQD8D0160FPHBDM4G079GS":3,"skill-same:01M6KQD8D0160FPHBDM4G079GS":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":13,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6KQD8D0160FPHBDM4G079GS","evaluating-cosmos-policy",null,"Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Robotics\u002Fcosmos-policy\u002Fcosmos-policy\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"policy","evaluating cosmos policy","Cosmos Policy","cosmos","running","when","for","orchestra","headless","environments","rendering","cluster","latency","local","robotics","up","use","simulation","Transformers","NVIDIA","Evaluation","git","2026-11-10 10:12:30",false,"---\nname: evaluating-cosmos-policy\ndescription: Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Cosmos Policy, VLA, Robotics, LIBERO, RoboCasa, Simulation, Evaluation, Profiling, EGL Rendering]\ndependencies: [torch>=2.1.0, mujoco>=3.0.0, robosuite>=1.4.0, \"robocasa @ git+https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git\", transformers>=4.40.0, \"cosmos-policy @ git+https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy.git\"]\n---\n\n# Cosmos Policy Evaluation\n\nEvaluation workflows for NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments from the public `cosmos-policy` repository. Covers blank-machine setup, headless GPU evaluation, and inference profiling.\n\n## Quick start\n\nRun a minimal LIBERO evaluation using the official public eval module:\n\n```bash\nuv run --extra cu128 --group libero --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n    --config cosmos_predict2_2b_480p_libero__inference_only \\\n    --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 16 \\\n    --num_open_loop_steps 16 \\\n    --task_suite_name libero_10 \\\n    --num_trials_per_task 1 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --ar_future_prediction False \\\n    --ar_value_prediction False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n## Core concepts\n\n**What Cosmos Policy is**: NVIDIA Cosmos Policy is a vision-language-action (VLA) model that uses Cosmos Tokenizer to encode visual observations into discrete tokens, then predicts robot actions conditioned on language instructions and visual context.\n\n**Key architecture choices**:\n\n| Component | Design |\n|-----------|--------|\n| Visual encoder | Cosmos Tokenizer (discrete tokens) |\n| Language conditioning | Cross-attention to language embeddings |\n| Action prediction | Autoregressive action token generation |\n\n**Public command surface**: The supported evaluation entrypoints are `cosmos_policy.experiments.robot.libero.run_libero_eval` and `cosmos_policy.experiments.robot.robocasa.run_robocasa_eval`. Keep reproduction notes anchored to these public modules and their documented flags.\n\n## Compute requirements\n\n| Task | GPU | VRAM | Typical wall time |\n|------|-----|------|-------------------|\n| LIBERO smoke eval (1 trial) | 1x A40\u002FA100 | ~16 GB | 5-10 min |\n| LIBERO full eval (50 trials) | 1x A40\u002FA100 | ~16 GB | 2-4 hours |\n| RoboCasa single-task (2 trials) | 1x A40\u002FA100 | ~18 GB | 10-15 min |\n| RoboCasa all-tasks | 1x A40\u002FA100 | ~18 GB | 4-8 hours |\n\n## When to use vs alternatives\n\n**Use this skill when:**\n- Evaluating NVIDIA Cosmos Policy on LIBERO or RoboCasa benchmarks\n- Profiling inference latency and throughput for Cosmos Policy\n- Setting up headless EGL rendering for robot simulation on GPU clusters\n\n**Use alternatives when:**\n- Training or fine-tuning Cosmos Policy from scratch (use official Cosmos training docs)\n- Working with OpenVLA-based policies (use `fine-tuning-openvla-oft`)\n- Working with Physical Intelligence pi0 models (use `fine-tuning-serving-openpi`)\n- Running real-robot evaluation rather than simulation\n\n---\n\n## Workflow 1: LIBERO evaluation\n\nCopy this checklist and track progress:\n\n```text\nLIBERO Eval Progress:\n- [ ] Step 1: Install environment and dependencies\n- [ ] Step 2: Configure headless EGL rendering\n- [ ] Step 3: Run smoke evaluation\n- [ ] Step 4: Validate outputs and parse results\n- [ ] Step 5: Run full benchmark if smoke passes\n```\n\n**Step 1: Install environment**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy.git\ncd cosmos-policy\n# Follow SETUP.md to build and enter the supported Docker container.\n# Then, inside the container:\nuv sync --extra cu128 --group libero --python 3.10\n```\n\n**Step 2: Configure headless rendering**\n\n```bash\nexport CUDA_VISIBLE_DEVICES=0\nexport MUJOCO_EGL_DEVICE_ID=0\nexport MUJOCO_GL=egl\nexport PYOPENGL_PLATFORM=egl\n```\n\n**Step 3: Run smoke evaluation**\n\n```bash\nuv run --extra cu128 --group libero --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n    --config cosmos_predict2_2b_480p_libero__inference_only \\\n    --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 16 \\\n    --num_open_loop_steps 16 \\\n    --task_suite_name libero_10 \\\n    --num_trials_per_task 1 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --ar_future_prediction False \\\n    --ar_value_prediction False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n**Step 4: Validate and parse results**\n\n```python\nimport json\nimport glob\n\n# Find latest evaluation result from the official log directory\nlog_files = sorted(glob.glob(\"cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F**\u002F*.json\", recursive=True))\nwith open(log_files[-1]) as f:\n    results = json.load(f)\n\nprint(results)\n```\n\n**Step 5: Scale up**\n\nRun across all four LIBERO task suites with 50 trials:\n\n```bash\nfor suite in libero_spatial libero_object libero_goal libero_10; do\n  uv run --extra cu128 --group libero --python 3.10 \\\n    python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n      --config cosmos_predict2_2b_480p_libero__inference_only \\\n      --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n      --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n      --use_wrist_image True \\\n      --use_proprio True \\\n      --normalize_proprio True \\\n      --unnormalize_actions True \\\n      --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n      --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n      --trained_with_image_aug True \\\n      --chunk_size 16 \\\n      --num_open_loop_steps 16 \\\n      --task_suite_name \"$suite\" \\\n      --num_trials_per_task 50 \\\n      --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n      --seed 195 \\\n      --randomize_seed False \\\n      --deterministic True \\\n      --run_id_note \"suite_${suite}\" \\\n      --ar_future_prediction False \\\n      --ar_value_prediction False \\\n      --use_jpeg_compression True \\\n      --flip_images True \\\n      --num_denoising_steps_action 5 \\\n      --num_denoising_steps_future_state 1 \\\n      --num_denoising_steps_value 1 \\\n      --data_collection False\ndone\n```\n\n---\n\n## Workflow 2: RoboCasa evaluation\n\nCopy this checklist and track progress:\n\n```text\nRoboCasa Eval Progress:\n- [ ] Step 1: Install RoboCasa assets and verify macros\n- [ ] Step 2: Run single-task smoke evaluation\n- [ ] Step 3: Validate outputs\n- [ ] Step 4: Expand to multi-task runs\n```\n\n**Step 1: Install RoboCasa**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git\nuv pip install -e robocasa-cosmos-policy\npython -m robocasa.scripts.setup_macros\npython -m robocasa.scripts.download_kitchen_assets\n```\n\nThis fork installs the `robocasa` Python package expected by Cosmos Policy while preserving the patched environment changes used in the public RoboCasa eval path. Verify `macros_private.py` exists and paths are correct.\n\n**Step 2: Single-task smoke evaluation**\n\n```bash\nuv run --extra cu128 --group robocasa --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.robocasa.run_robocasa_eval \\\n    --config cosmos_predict2_2b_480p_robocasa_50_demos_per_task__inference \\\n    --ckpt_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --num_wrist_images 1 \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B\u002Frobocasa_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B\u002Frobocasa_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 32 \\\n    --num_open_loop_steps 16 \\\n    --task_name TurnOffMicrowave \\\n    --obj_instance_split A \\\n    --num_trials_per_task 2 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Frobocasa\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --use_variance_scale False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n**Step 3: Validate outputs**\n\n- Confirm the eval log prints the expected task name, object split, and checkpoint\u002Fconfig values.\n- Inspect the final `Success rate:` line in the log.\n\n**Step 4: Expand scope**\n\nIncrease `--num_trials_per_task` or add more tasks. Keep `--obj_instance_split` fixed across repeated runs for comparability.\n\n---\n\n## Workflow 3: Blank-machine cluster launch\n\n```text\nCluster Launch Progress:\n- [ ] Step 1: Clone the public repo and enter the supported runtime\n- [ ] Step 2: Sync the benchmark-specific dependency group\n- [ ] Step 3: Export rendering and cache environment variables before eval\n```\n\n**Step 1: Clone and enter the supported runtime**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy.git\ncd cosmos-policy\n# Follow SETUP.md, start the Docker container, and enter it before continuing.\n```\n\n**Step 2: Sync dependencies**\n\n```bash\nuv sync --extra cu128 --group libero --python 3.10\n# or, for RoboCasa:\nuv sync --extra cu128 --group robocasa --python 3.10\n# then install the Cosmos-compatible RoboCasa fork:\ngit clone https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git\nuv pip install -e robocasa-cosmos-policy\n```\n\n**Step 3: Export runtime environment**\n\n```bash\nexport CUDA_VISIBLE_DEVICES=0\nexport MUJOCO_EGL_DEVICE_ID=0\nexport MUJOCO_GL=egl\nexport PYOPENGL_PLATFORM=egl\nexport HF_HOME=${HF_HOME:-$HOME\u002F.cache\u002Fhuggingface}\nexport TRANSFORMERS_CACHE=${TRANSFORMERS_CACHE:-$HF_HOME}\n```\n\n---\n\n## Expected performance benchmarks\n\nReference values from official evaluation (tied to specific setup and seeds):\n\n| Task Suite | Success Rate | Notes |\n|-----------|-------------|-------|\n| LIBERO-Spatial | 98.1% | Official LIBERO spatial result |\n| LIBERO-Object | 100.0% | Official LIBERO object result |\n| LIBERO-Goal | 98.2% | Official LIBERO goal result |\n| LIBERO-Long | 97.6% | Official LIBERO long-horizon result |\n| LIBERO-Average | 98.5% | Official average across LIBERO suites |\n| RoboCasa | 67.1% | Official RoboCasa average result |\n\n**Reproduction note**: Published success rates still depend on checkpoint choice, task suite, seeds, and simulator setup. Record the exact command and environment alongside any reported number.\n\n---\n\n## Non-negotiable rules\n\n- **EGL alignment**: Always set `CUDA_VISIBLE_DEVICES`, `MUJOCO_EGL_DEVICE_ID`, `MUJOCO_GL=egl`, and `PYOPENGL_PLATFORM=egl` together on headless GPU nodes.\n- **Official runtime first**: If host-Python installs hit binary compatibility issues, fall back to the supported container workflow from `SETUP.md` before debugging package internals.\n- **Cache consistency**: Use the same cache directory across setup and eval so Hugging Face and dependency caches are reused.\n- **Run comparability**: Keep task name, object split, seed, and trial count fixed across repeated runs.\n\n---\n\n## Common issues\n\n**Issue: binary compatibility or loader failures on host Python**\n\nFix: rerun inside the official container\u002Fruntime from `SETUP.md`. Do not assume host-package rebuilds will match the public release environment.\n\n**Issue: LIBERO prompts for config path in a non-interactive shell**\n\nFix: pre-create `LIBERO_CONFIG_PATH\u002Fconfig.yaml`:\n\n```python\nimport os, yaml\n\nconfig_dir = os.path.expanduser(\"~\u002F.libero\")\nos.makedirs(config_dir, exist_ok=True)\nwith open(os.path.join(config_dir, \"config.yaml\"), \"w\") as f:\n    yaml.dump({\"benchmark_root\": \"\u002Fpath\u002Fto\u002Flibero\u002Fdatasets\"}, f)\n```\n\n**Issue: EGL initialization or shutdown noise**\n\nFix: align EGL environment variables first. Treat teardown-only `EGL_NOT_INITIALIZED` warnings as low-signal unless the job exits non-zero.\n\n**Issue: Kitchen object sampling NaNs or asset lookup failures in RoboCasa**\n\nFix: rerun asset setup and confirm the patched robocasa install is intact:\n\n```bash\npython -m robocasa.scripts.download_kitchen_assets\npython -c \"import robocasa; print(robocasa.__file__)\"\n```\n\n**Issue: MuJoCo rendering mismatch**\n\nFix: verify GPU device alignment:\n\n```python\nimport os\ncuda_dev = os.environ.get(\"CUDA_VISIBLE_DEVICES\", \"not set\")\negl_dev = os.environ.get(\"MUJOCO_EGL_DEVICE_ID\", \"not set\")\nassert cuda_dev == egl_dev, f\"GPU mismatch: CUDA={cuda_dev}, EGL={egl_dev}\"\nprint(f\"Rendering on GPU {cuda_dev}\")\n```\n\n---\n\n## Advanced topics\n\n**LIBERO command matrix**: See [references\u002Flibero-commands.md](references\u002Flibero-commands.md)\n**RoboCasa command matrix**: See [references\u002Frobocasa-commands.md](references\u002Frobocasa-commands.md)\n\n## Resources\n\n- Cosmos Policy repository: https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy\n- LIBERO benchmark: https:\u002F\u002Fgithub.com\u002FLifelong-Robot-Learning\u002FLIBERO\n- Cosmos-compatible RoboCasa fork: https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy\n- Upstream RoboCasa project: https:\u002F\u002Fgithub.com\u002Frobocasa\u002Frobocasa\n- MuJoCo documentation: https:\u002F\u002Fmujoco.readthedocs.io\u002F\n","# Cosmos Policy Evaluation\n\nEvaluation workflows for NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments from the public `cosmos-policy` repository. Covers blank-machine setup, headless GPU evaluation, and inference profiling.\n\n## Quick start\n\nRun a minimal LIBERO evaluation using the official public eval module:\n\n```bash\nuv run --extra cu128 --group libero --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n    --config cosmos_predict2_2b_480p_libero__inference_only \\\n    --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 16 \\\n    --num_open_loop_steps 16 \\\n    --task_suite_name libero_10 \\\n    --num_trials_per_task 1 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --ar_future_prediction False \\\n    --ar_value_prediction False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n## Core concepts\n\n**What Cosmos Policy is**: NVIDIA Cosmos Policy is a vision-language-action (VLA) model that uses Cosmos Tokenizer to encode visual observations into discrete tokens, then predicts robot actions conditioned on language instructions and visual context.\n\n**Key architecture choices**:\n\n| Component | Design |\n|-----------|--------|\n| Visual encoder | Cosmos Tokenizer (discrete tokens) |\n| Language conditioning | Cross-attention to language embeddings |\n| Action prediction | Autoregressive action token generation |\n\n**Public command surface**: The supported evaluation entrypoints are `cosmos_policy.experiments.robot.libero.run_libero_eval` and `cosmos_policy.experiments.robot.robocasa.run_robocasa_eval`. Keep reproduction notes anchored to these public modules and their documented flags.\n\n## Compute requirements\n\n| Task | GPU | VRAM | Typical wall time |\n|------|-----|------|-------------------|\n| LIBERO smoke eval (1 trial) | 1x A40\u002FA100 | ~16 GB | 5-10 min |\n| LIBERO full eval (50 trials) | 1x A40\u002FA100 | ~16 GB | 2-4 hours |\n| RoboCasa single-task (2 trials) | 1x A40\u002FA100 | ~18 GB | 10-15 min |\n| RoboCasa all-tasks | 1x A40\u002FA100 | ~18 GB | 4-8 hours |\n\n## When to use vs alternatives\n\n**Use this skill when:**\n- Evaluating NVIDIA Cosmos Policy on LIBERO or RoboCasa benchmarks\n- Profiling inference latency and throughput for Cosmos Policy\n- Setting up headless EGL rendering for robot simulation on GPU clusters\n\n**Use alternatives when:**\n- Training or fine-tuning Cosmos Policy from scratch (use official Cosmos training docs)\n- Working with OpenVLA-based policies (use `fine-tuning-openvla-oft`)\n- Working with Physical Intelligence pi0 models (use `fine-tuning-serving-openpi`)\n- Running real-robot evaluation rather than simulation\n\n---\n\n## Workflow 1: LIBERO evaluation\n\nCopy this checklist and track progress:\n\n```text\nLIBERO Eval Progress:\n- [ ] Step 1: Install environment and dependencies\n- [ ] Step 2: Configure headless EGL rendering\n- [ ] Step 3: Run smoke evaluation\n- [ ] Step 4: Validate outputs and parse results\n- [ ] Step 5: Run full benchmark if smoke passes\n```\n\n**Step 1: Install environment**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy.git\ncd cosmos-policy\n# Follow SETUP.md to build and enter the supported Docker container.\n# Then, inside the container:\nuv sync --extra cu128 --group libero --python 3.10\n```\n\n**Step 2: Configure headless rendering**\n\n```bash\nexport CUDA_VISIBLE_DEVICES=0\nexport MUJOCO_EGL_DEVICE_ID=0\nexport MUJOCO_GL=egl\nexport PYOPENGL_PLATFORM=egl\n```\n\n**Step 3: Run smoke evaluation**\n\n```bash\nuv run --extra cu128 --group libero --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n    --config cosmos_predict2_2b_480p_libero__inference_only \\\n    --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 16 \\\n    --num_open_loop_steps 16 \\\n    --task_suite_name libero_10 \\\n    --num_trials_per_task 1 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --ar_future_prediction False \\\n    --ar_value_prediction False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n**Step 4: Validate and parse results**\n\n```python\nimport json\nimport glob\n\n# Find latest evaluation result from the official log directory\nlog_files = sorted(glob.glob(\"cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F**\u002F*.json\", recursive=True))\nwith open(log_files[-1]) as f:\n    results = json.load(f)\n\nprint(results)\n```\n\n**Step 5: Scale up**\n\nRun across all four LIBERO task suites with 50 trials:\n\n```bash\nfor suite in libero_spatial libero_object libero_goal libero_10; do\n  uv run --extra cu128 --group libero --python 3.10 \\\n    python -m cosmos_policy.experiments.robot.libero.run_libero_eval \\\n      --config cosmos_predict2_2b_480p_libero__inference_only \\\n      --ckpt_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B \\\n      --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n      --use_wrist_image True \\\n      --use_proprio True \\\n      --normalize_proprio True \\\n      --unnormalize_actions True \\\n      --dataset_stats_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_dataset_statistics.json \\\n      --t5_text_embeddings_path nvidia\u002FCosmos-Policy-LIBERO-Predict2-2B\u002Flibero_t5_embeddings.pkl \\\n      --trained_with_image_aug True \\\n      --chunk_size 16 \\\n      --num_open_loop_steps 16 \\\n      --task_suite_name \"$suite\" \\\n      --num_trials_per_task 50 \\\n      --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Flibero\u002Flogs\u002F \\\n      --seed 195 \\\n      --randomize_seed False \\\n      --deterministic True \\\n      --run_id_note \"suite_${suite}\" \\\n      --ar_future_prediction False \\\n      --ar_value_prediction False \\\n      --use_jpeg_compression True \\\n      --flip_images True \\\n      --num_denoising_steps_action 5 \\\n      --num_denoising_steps_future_state 1 \\\n      --num_denoising_steps_value 1 \\\n      --data_collection False\ndone\n```\n\n---\n\n## Workflow 2: RoboCasa evaluation\n\nCopy this checklist and track progress:\n\n```text\nRoboCasa Eval Progress:\n- [ ] Step 1: Install RoboCasa assets and verify macros\n- [ ] Step 2: Run single-task smoke evaluation\n- [ ] Step 3: Validate outputs\n- [ ] Step 4: Expand to multi-task runs\n```\n\n**Step 1: Install RoboCasa**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git\nuv pip install -e robocasa-cosmos-policy\npython -m robocasa.scripts.setup_macros\npython -m robocasa.scripts.download_kitchen_assets\n```\n\nThis fork installs the `robocasa` Python package expected by Cosmos Policy while preserving the patched environment changes used in the public RoboCasa eval path. Verify `macros_private.py` exists and paths are correct.\n\n**Step 2: Single-task smoke evaluation**\n\n```bash\nuv run --extra cu128 --group robocasa --python 3.10 \\\n  python -m cosmos_policy.experiments.robot.robocasa.run_robocasa_eval \\\n    --config cosmos_predict2_2b_480p_robocasa_50_demos_per_task__inference \\\n    --ckpt_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B \\\n    --config_file cosmos_policy\u002Fconfig\u002Fconfig.py \\\n    --use_wrist_image True \\\n    --num_wrist_images 1 \\\n    --use_proprio True \\\n    --normalize_proprio True \\\n    --unnormalize_actions True \\\n    --dataset_stats_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B\u002Frobocasa_dataset_statistics.json \\\n    --t5_text_embeddings_path nvidia\u002FCosmos-Policy-RoboCasa-Predict2-2B\u002Frobocasa_t5_embeddings.pkl \\\n    --trained_with_image_aug True \\\n    --chunk_size 32 \\\n    --num_open_loop_steps 16 \\\n    --task_name TurnOffMicrowave \\\n    --obj_instance_split A \\\n    --num_trials_per_task 2 \\\n    --local_log_dir cosmos_policy\u002Fexperiments\u002Frobot\u002Frobocasa\u002Flogs\u002F \\\n    --seed 195 \\\n    --randomize_seed False \\\n    --deterministic True \\\n    --run_id_note smoke \\\n    --use_variance_scale False \\\n    --use_jpeg_compression True \\\n    --flip_images True \\\n    --num_denoising_steps_action 5 \\\n    --num_denoising_steps_future_state 1 \\\n    --num_denoising_steps_value 1 \\\n    --data_collection False\n```\n\n**Step 3: Validate outputs**\n\n- Confirm the eval log prints the expected task name, object split, and checkpoint\u002Fconfig values.\n- Inspect the final `Success rate:` line in the log.\n\n**Step 4: Expand scope**\n\nIncrease `--num_trials_per_task` or add more tasks. Keep `--obj_instance_split` fixed across repeated runs for comparability.\n\n---\n\n## Workflow 3: Blank-machine cluster launch\n\n```text\nCluster Launch Progress:\n- [ ] Step 1: Clone the public repo and enter the supported runtime\n- [ ] Step 2: Sync the benchmark-specific dependency group\n- [ ] Step 3: Export rendering and cache environment variables before eval\n```\n\n**Step 1: Clone and enter the supported runtime**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy.git\ncd cosmos-policy\n# Follow SETUP.md, start the Docker container, and enter it before continuing.\n```\n\n**Step 2: Sync dependencies**\n\n```bash\nuv sync --extra cu128 --group libero --python 3.10\n# or, for RoboCasa:\nuv sync --extra cu128 --group robocasa --python 3.10\n# then install the Cosmos-compatible RoboCasa fork:\ngit clone https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git\nuv pip install -e robocasa-cosmos-policy\n```\n\n**Step 3: Export runtime environment**\n\n```bash\nexport CUDA_VISIBLE_DEVICES=0\nexport MUJOCO_EGL_DEVICE_ID=0\nexport MUJOCO_GL=egl\nexport PYOPENGL_PLATFORM=egl\nexport HF_HOME=${HF_HOME:-$HOME\u002F.cache\u002Fhuggingface}\nexport TRANSFORMERS_CACHE=${TRANSFORMERS_CACHE:-$HF_HOME}\n```\n\n---\n\n## Expected performance benchmarks\n\nReference values from official evaluation (tied to specific setup and seeds):\n\n| Task Suite | Success Rate | Notes |\n|-----------|-------------|-------|\n| LIBERO-Spatial | 98.1% | Official LIBERO spatial result |\n| LIBERO-Object | 100.0% | Official LIBERO object result |\n| LIBERO-Goal | 98.2% | Official LIBERO goal result |\n| LIBERO-Long | 97.6% | Official LIBERO long-horizon result |\n| LIBERO-Average | 98.5% | Official average across LIBERO suites |\n| RoboCasa | 67.1% | Official RoboCasa average result |\n\n**Reproduction note**: Published success rates still depend on checkpoint choice, task suite, seeds, and simulator setup. Record the exact command and environment alongside any reported number.\n\n---\n\n## Non-negotiable rules\n\n- **EGL alignment**: Always set `CUDA_VISIBLE_DEVICES`, `MUJOCO_EGL_DEVICE_ID`, `MUJOCO_GL=egl`, and `PYOPENGL_PLATFORM=egl` together on headless GPU nodes.\n- **Official runtime first**: If host-Python installs hit binary compatibility issues, fall back to the supported container workflow from `SETUP.md` before debugging package internals.\n- **Cache consistency**: Use the same cache directory across setup and eval so Hugging Face and dependency caches are reused.\n- **Run comparability**: Keep task name, object split, seed, and trial count fixed across repeated runs.\n\n---\n\n## Common issues\n\n**Issue: binary compatibility or loader failures on host Python**\n\nFix: rerun inside the official container\u002Fruntime from `SETUP.md`. Do not assume host-package rebuilds will match the public release environment.\n\n**Issue: LIBERO prompts for config path in a non-interactive shell**\n\nFix: pre-create `LIBERO_CONFIG_PATH\u002Fconfig.yaml`:\n\n```python\nimport os, yaml\n\nconfig_dir = os.path.expanduser(\"~\u002F.libero\")\nos.makedirs(config_dir, exist_ok=True)\nwith open(os.path.join(config_dir, \"config.yaml\"), \"w\") as f:\n    yaml.dump({\"benchmark_root\": \"\u002Fpath\u002Fto\u002Flibero\u002Fdatasets\"}, f)\n```\n\n**Issue: EGL initialization or shutdown noise**\n\nFix: align EGL environment variables first. Treat teardown-only `EGL_NOT_INITIALIZED` warnings as low-signal unless the job exits non-zero.\n\n**Issue: Kitchen object sampling NaNs or asset lookup failures in RoboCasa**\n\nFix: rerun asset setup and confirm the patched robocasa install is intact:\n\n```bash\npython -m robocasa.scripts.download_kitchen_assets\npython -c \"import robocasa; print(robocasa.__file__)\"\n```\n\n**Issue: MuJoCo rendering mismatch**\n\nFix: verify GPU device alignment:\n\n```python\nimport os\ncuda_dev = os.environ.get(\"CUDA_VISIBLE_DEVICES\", \"not set\")\negl_dev = os.environ.get(\"MUJOCO_EGL_DEVICE_ID\", \"not set\")\nassert cuda_dev == egl_dev, f\"GPU mismatch: CUDA={cuda_dev}, EGL={egl_dev}\"\nprint(f\"Rendering on GPU {cuda_dev}\")\n```\n\n---\n\n## Advanced topics\n\n**LIBERO command matrix**: See [references\u002Flibero-commands.md](references\u002Flibero-commands.md)\n**RoboCasa command matrix**: See [references\u002Frobocasa-commands.md](references\u002Frobocasa-commands.md)\n\n## Resources\n\n- Cosmos Policy repository: https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fcosmos-policy\n- LIBERO benchmark: https:\u002F\u002Fgithub.com\u002FLifelong-Robot-Learning\u002FLIBERO\n- Cosmos-compatible RoboCasa fork: https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy\n- Upstream RoboCasa project: https:\u002F\u002Fgithub.com\u002Frobocasa\u002Frobocasa\n- MuJoCo documentation: https:\u002F\u002Fmujoco.readthedocs.io\u002F\n",{"name":5,"tags":45,"author":53,"license":14,"version":54,"description":7,"dependencies":55},[20,46,47,48,49,50,38,51,52],"VLA","Robotics","LIBERO","RoboCasa","Simulation","Profiling","EGL Rendering","Orchestra Research","1.0.0",[56,57,58,59,60,61],"torch>=2.1.0","mujoco>=3.0.0","robosuite>=1.4.0","robocasa @ git+https:\u002F\u002Fgithub.com\u002Fmoojink\u002Frobocasa-cosmos-policy.git","transformers>=4.40.0","cosmos-policy @ 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[160,161],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":133,"categoryName":134,"taxonomyType":103,"taxonomyTypeDesc":104},[163,164,165,166,23,167,168,169,170,34,171,172,173,24,174,138],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":40},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[177,183],"文本",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":188,"sourceHtmlUrl":189,"categories":190,"keywords":191,"publishedAt":40},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[186,192,193,194,195,196,34,197,114,150,198,199,200,201,116,202,203,204,205,23,206,207,163],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":212,"sourceHtmlUrl":213,"categories":214,"keywords":216,"publishedAt":40},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[215],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[217,218,219,114,113,112,138,109,183,220,163,165],"humanizer","zh","humanizer zh","翻译"]