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docs/pages/example_workflows/locomanipulation/step_5_evaluation.rst

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@@ -104,6 +104,7 @@ Parallel evaluation of the policy in multiple parallel environments is also supp
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.. code-block:: bash
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python isaaclab_arena/evaluation/policy_runner.py \
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--viz kit \
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--policy_type isaaclab_arena_gr00t.policy.gr00t_closedloop_policy.Gr00tClosedloopPolicy \
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--policy_config_yaml_path isaaclab_arena_gr00t/policy/config/g1_locomanip_gr00t_closedloop_config.yaml \
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--num_steps 1200 \

docs/pages/example_workflows/sequential_static_manipulation/step_5_evaluation.rst

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This workflow demonstrates running the trained GR00T N1.6 policy in closed-loop
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and evaluating it in Arena GR1 Open Microwave Door Task environment.
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and evaluating it in the GR1 Sequential Pick & Place and Close Door Environment.
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**Docker Container**: Base + GR00T (see :doc:`../imitation_learning/index` for more details)
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