feat(sim): Add Libero Env (#1984)

tests/envs/test_envs.py

@@ -46,7 +46,10 @@ def test_env(env_name, env_task, obs_type):
 @require_env
 def test_factory(env_name):
     cfg = make_env_config(env_name)
-    env = make_env(cfg, n_envs=1)
+    envs = make_env(cfg, n_envs=1)
+    suite_name = next(iter(envs))
+    task_id = next(iter(envs[suite_name]))
+    env = envs[suite_name][task_id]
     obs, _ = env.reset()
     obs = preprocess_observation(obs)
 

tests/policies/test_policies.py

@@ -159,7 +159,7 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     assert isinstance(policy, PreTrainedPolicy)
 
     # Check that we run select_actions and get the appropriate output.
-    env = make_env(train_cfg.env, n_envs=2)
+    envs = make_env(train_cfg.env, n_envs=2)
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
@@ -188,6 +188,12 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
 
     # reset the policy and environment
     policy.reset()
+    # For testing purposes, we only need a single environment instance.
+    # So here we unwrap the first suite_name and first task_id to grab
+    # the actual env object (SyncVectorEnv) that exposes `.reset()`.
+    suite_name = next(iter(envs))
+    task_id = next(iter(envs[suite_name]))
+    env = envs[suite_name][task_id]
     observation, _ = env.reset(seed=train_cfg.seed)
 
     # apply transform to normalize the observations