evaluating-cosmos-policy
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill cosmos-policy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cosmos Policy Evaluation Evaluation 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. ## Quick start Run a minimal LIBERO evaluation using the official public eval module: ```bash uv run --extra cu128 --group libero --python 3.10 \ python -m cosmos_policy.experiments.robot.libero.run_libero_eval \ --config cosmos_predict2_2b_480p_libero__inference_only \ --ckpt_path nvidia/Cosmos-Policy-LIBERO-Predict2-2B \ --config_file cosmos_policy/config/config.py \ --use_wrist_image True \ --use_proprio True \ --normalize_proprio True \ --unnormalize_actions True \ --dataset_stats_path nvidia/Cosmos-Policy-LIBERO-Predict2-2B/libero_dataset_statistics.json \ --t5_text_embeddings_path nvidia/Cosmos-Policy-LIBERO-Predict2-2B/libero_t5_embeddings.pkl \ --trained_with_image_aug True \ --chunk_size 16 \ --num_open_loop_steps 16 \ --task_suite_name libero_10 \ --num_trials_per_task 1 \ --local_log_dir cosmos_policy/experiments/robot/libero/logs/ \ --seed 195 \ --randomize_seed False \ --deterministic True \ --run_id_note smoke \ --ar_future_predic
- Quick start
- Core concepts
- Compute requirements
- When to use vs alternatives
- Workflow 1: LIBERO evaluation
- Workflow 2: RoboCasa evaluation
- Workflow 3: Blank-machine cluster launch
- Expected performance benchmarks
- Non-negotiable rules
- Common issues
- Advanced topics
- Resources
uv run --extra cu128 --group libero --python 3.10 \ python -m cosmos_policy.experiments.robot.libero.run_libero_eval \ git clone https://github.com/NVlabs/cosmos-policy.git cd cosmos-policy Follow SETUP.md to build and enter the supported Docker container. uv sync --extra cu128 --group libero --python 3.10 export CUDA_VISIBLE_DEVICES=0 export MUJOCO_EGL_DEVICE_ID=0 export MUJOCO_GL=egl export PYOPENGL_PLATFORM=egl
What does the evaluating-cosmos-policy skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill cosmos-policy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.