diff --git a/metaworld/evaluation.py b/metaworld/evaluation.py
index b4328f2a5..e80314204 100644
--- a/metaworld/evaluation.py
+++ b/metaworld/evaluation.py
@@ -5,6 +5,7 @@
 import gymnasium as gym
 import numpy as np
 import numpy.typing as npt
+from tqdm import tqdm
 
 from metaworld.env_dict import ALL_V3_ENVIRONMENTS
 
@@ -63,6 +64,9 @@ def evaluation(
         task_name: [] for task_name in set(task_names)
     }
 
+    pbar_envs_done = tqdm(total=len(set(task_names)) *
+                          num_episodes, desc="Evaluation")
+
     def eval_done(returns):
         return all(len(r) >= num_episodes for _, r in returns.items())
 
@@ -75,6 +79,10 @@ def eval_done(returns):
 
         for i, env_ended in enumerate(dones):
             if env_ended:
+                task = task_names[i]
+                current_count = len(episodic_returns[task])
+                if current_count < num_episodes:
+                    pbar_envs_done.update(1)
                 episodic_returns[task_names[i]].append(
                     float(infos["final_info"]["episode"]["r"][i])
                 )
diff --git a/metaworld/policies/sawyer_peg_insertion_side_v3_policy.py b/metaworld/policies/sawyer_peg_insertion_side_v3_policy.py
index 3763a4576..902639755 100644
--- a/metaworld/policies/sawyer_peg_insertion_side_v3_policy.py
+++ b/metaworld/policies/sawyer_peg_insertion_side_v3_policy.py
@@ -44,14 +44,21 @@ def _desired_pos(o_d: dict[str, npt.NDArray[np.float64]]) -> npt.NDArray[Any]:
         # Z is constant at .16
         pos_hole = np.array([-0.35, o_d["goal_pos"][1], 0.16])
 
+        gripper_dist = o_d["gripper_distance_apart"]
+
+        # gets rid of a tiny bump
+        hole_z_offset = np.array([0.0, 0.0, 0.01])
+
         if np.linalg.norm(pos_curr[:2] - pos_peg[:2]) > 0.04:
+            # move to pickup position above peg
             return pos_peg + np.array([0.0, 0.0, 0.3])
-        elif abs(pos_curr[2] - pos_peg[2]) > 0.025:
+        elif abs(pos_curr[2] - pos_peg[2]) > 0.025 and gripper_dist > 0.5:
+            # move down to peg only if not holding
             return pos_peg
         elif np.linalg.norm(pos_peg[1:] - pos_hole[1:]) > 0.03:
-            return pos_hole + np.array([0.4, 0.0, 0.0])
+            return pos_hole + np.array([0.4, 0.0, 0.0]) + hole_z_offset
         else:
-            return pos_hole
+            return pos_hole + hole_z_offset
 
     @staticmethod
     def _grab_effort(o_d: dict[str, npt.NDArray[np.float64]]) -> float:
diff --git a/pyproject.toml b/pyproject.toml
index 7ecf513aa..f8931edf7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,12 +28,18 @@ dependencies = [
     "mujoco>=3.0.0",
     "numpy>=1.18",
     "scipy>=1.4.1",
-    "imageio"
+    "imageio",
 ]
 
 [project.optional-dependencies]
 # Update dependencies in `all` if any are added or removed
-testing = ["ipdb", "memory_profiler", "pyquaternion==0.9.5", "pytest>=4.4.0"]
+testing = [
+    "ipdb",
+    "memory_profiler",
+    "pyquaternion==0.9.5",
+    "pytest>=4.4.0",
+    "tqdm",
+]
 dev = ["black", "isort", "mypy"]
 
 [project.urls]
diff --git a/tests/metaworld/test_evaluation.py b/tests/metaworld/test_evaluation.py
index f67e587fd..8c97cfd7b 100644
--- a/tests/metaworld/test_evaluation.py
+++ b/tests/metaworld/test_evaluation.py
@@ -95,14 +95,29 @@ def test_evaluation():
         max_episode_steps=max_episode_steps,
         vector_strategy="async",
     )
+    num_envs = envs.num_envs
     agent = ScriptedPolicyAgent(envs)
     mean_success_rate, mean_returns, success_rate_per_task, _ = evaluation.evaluation(
         agent, envs, num_episodes=num_episodes
     )
+    envs.close()
     assert isinstance(mean_returns, float)
-    assert mean_success_rate >= 0.80
-    assert len(success_rate_per_task) == envs.num_envs
-    assert np.all(np.array(list(success_rate_per_task.values())) >= 0.80)
+    assert len(success_rate_per_task) == num_envs
+    worst_accepted_fail_rate = 0.8
+    failed_envs_names = []
+    failed_envs_rates = []
+    for task_name, success_rate in success_rate_per_task.items():
+        if success_rate < worst_accepted_fail_rate:
+            failed_envs_names.append(task_name)
+            failed_envs_rates.append(success_rate)
+    if len(failed_envs_names) > 0:
+        print(
+            f"The following environments failed the success rate threshold of {worst_accepted_fail_rate*100}%:"
+        )
+        for name, rate in zip(failed_envs_names, failed_envs_rates):
+            print(f"- {name}: {rate*100}% success rate")
+        assert False, "Some environments did not meet the success rate threshold."
+    assert mean_success_rate >= worst_accepted_fail_rate
 
 
 # @pytest.mark.skip