Policy Closed-loop Evaluation

You can deploy the post-trained GR00T N1 policy for closed-loop control of the GR1 robot within an Isaac Lab environment, and benchmark its success rate in parallel runs.

Benchmarking Features

🚀 Parallelized Evaluation:

Isaac Lab supports parallelized environment instances for scalable benchmarking. Configure multiple parallel runs (e.g., 10–100 instances) to statistically quantify policy success rates under varying initial conditions.

Nut Pouring

Exhaust Pipe Sorting

✅ Success Metrics:

Task Completion: Binary success/failure based on object placement accuracy defined in the evaluation tasks. Success rates are logged in the teriminal per episode as,

==================================================
Successful trials: 9, out of 10 trials
Success rate: 0.9
==================================================

And the summary report as json file can be viewed as,

{
    "metadata": {
        "checkpoint_name": "gr00t-n1-2b-tuned",
        "seed": 10,
        "date": "2025-05-20 16:42:54"
    },
    "summary": {
        "successful_trials": 91,
        "total_rollouts": 100,
        "success_rate": 0.91
    }

Running Evaluation

Nut Pouring Task

To run parallel evaluation on the Nut Pouring task:

# Within IsaacLabEvalTasks directory
# Assume the post-trained policy checkpoints are under CKPTS_PATH
# Please use full path, instead of relative path for CKPTS_PATH
# export EVAL_RESULTS_FNAME="./eval_nutpouring.json"
python scripts/evaluate_gn1.py \
    --num_feedback_actions 16 \
    --num_envs 10 \
    --task_name nutpouring \
    --eval_file_path $EVAL_RESULTS_FNAME \
    --model_path $CKPTS_PATH \
    --rollout_length 30 \
    --seed 10 \
    --max_num_rollouts 100

Exhaust Pipe Sorting Task

To run parallel evaluation on the Exhaust Pipe Sorting task:

# Assume the post-trained policy checkpoints are under CKPTS_PATH
# Please use full path, instead of relative path for CKPTS_PATH
# export EVAL_RESULTS_FNAME="./eval_pipesorting.json"
python scripts/evaluate_gn1.py \
    --num_feedback_actions 16 \
    --num_envs 10 \
    --task_name pipesorting \
    --eval_file_path $EVAL_RESULTS_FNAME \
    --checkpoint_name gr00t-n1-2b-tuned-pipesorting \
    --model_path $CKPTS_PATH \
    --rollout_length 20 \
    --seed 10 \
    --max_num_rollouts 100

Performance Results

We report the success rate of evaluating tuned GR00T N1 policy over 200 trials, with random seed=15.

Evaluation Task	SR
Nut Pouring	91%
Exhaust Pipe Sorting	95%

Tips and Best Practices

💡 Tip:

Hardware requirement: Please follow the system requirements in Isaac Sim and Isaac GR00T to choose. The above evaluation results was reported on RTX A6000 Ada, Ubuntu 22.04.
num_feedback_actions determines the number of feedback actions to execute per inference, and it can be less than action_horizon. This option will impact the success rate of evaluation task even with the same checkpoint.
rollout_length impacts how many batched inference to make before task termination. Normally we set it between 20 to 30 for a faster turnaround.
num_envs decides the number of environments to run in parallel. Too many parallel environments (e.g. >100 on RTX A6000 Ada) will significantly slow down the UI rendering. We recommend to set between 10 to 30 for smooth rendering and efficient benchmarking.