Agent skill · research science · zechenzhangagi
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.
Why this skill is useful
Provides specific evaluation workflows and commands for NVIDIA Cosmos Policy that the AI wouldn't reliably generate on its own.
What it needs
Requires mujoco, robosuite, torch, transformers installed locally. About 6k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
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: Core concepts 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. Key architecture choices: Component Design ----------- -------- Visual encoder Cosmos Tokenizer (discrete tokens) Language conditioning Cross-attention to language embeddings Action prediction Autoregressive action token generation Public command surface: The supported evaluation entrypoints are cosmospolicy.experiments.robot.libero.runliberoeval and cosmospolicy.experiments.robot.robocasa.runrobocasaeval. Keep reproduction notes anchored to these public modules and their documented flags. Compute requirements Task GPU VRAM Typical wall time ------ ----- ------ ------------------- LIBERO smoke eval (1 trial) 1x A40/A100 ~16 GB 5-10 min LIBERO full eval (50 trials) 1x A40/A100 ~16 GB 2-4 hours RoboCasa single-task (2 trials) 1x A40/A100 ~18 GB 10-15 min RoboCasa all-tasks 1x A40/A100 ~18 GB 4-8 hours When to use vs alternatives Use this skill when: Evaluating NVIDIA Cosmos Policy on LIBERO or RoboCasa benchmarks Profiling inference latency and throughput for Cosmos Policy Setting up headless EGL rendering for robot simulation on GPU clusters Use alternatives when: Training or fine-tuning Cosmos Policy from scratch (use official Cosmos training docs) Working with OpenVLA-based policies (use fine-tuning-openvla-oft) Working with Physical Intelligence pi0 models (use fine-tuning-serving-openpi) Running real-robot evaluation rather than simulation --- Workflow 1: LIBERO evaluation Copy this checklis …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/cosmos-policy