diff --git a/CHANGELOG.md b/CHANGELOG.md
index 96a24f3dd1ba8..331185ee82403 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -261,6 +261,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed running sanity check with `RichProgressBar` ([#10913](https://github.com/PyTorchLightning/pytorch-lightning/pull/10913))
+
+
 - Fixed support for `CombinedLoader` while checking for warning raised with eval dataloaders ([#10994](https://github.com/PyTorchLightning/pytorch-lightning/pull/10994))
 
 
diff --git a/pytorch_lightning/callbacks/progress/rich_progress.py b/pytorch_lightning/callbacks/progress/rich_progress.py
index 50a017a72934c..fa4d925800d07 100644
--- a/pytorch_lightning/callbacks/progress/rich_progress.py
+++ b/pytorch_lightning/callbacks/progress/rich_progress.py
@@ -334,7 +334,8 @@ def on_sanity_check_start(self, trainer, pl_module):
 
     def on_sanity_check_end(self, trainer, pl_module):
         super().on_sanity_check_end(trainer, pl_module)
-        self._update(self.val_sanity_progress_bar_id, visible=False)
+        if self.progress is not None:
+            self.progress.update(self.val_sanity_progress_bar_id, advance=0, visible=False)
         self.refresh()
 
     def on_train_epoch_start(self, trainer, pl_module):
diff --git a/tests/callbacks/test_rich_progress_bar.py b/tests/callbacks/test_rich_progress_bar.py
index 5b0123e3060d3..f2e75006f7ecb 100644
--- a/tests/callbacks/test_rich_progress_bar.py
+++ b/tests/callbacks/test_rich_progress_bar.py
@@ -201,3 +201,24 @@ def test_rich_progress_bar_refresh_rate(progress_update, tmpdir, refresh_rate, e
     trainer.fit(model)
 
     assert progress_update.call_count == expected_call_count
+
+
+@RunIf(rich=True)
+@pytest.mark.parametrize("limit_val_batches", (1, 5))
+def test_rich_progress_bar_num_sanity_val_steps(tmpdir, limit_val_batches: int):
+    model = BoringModel()
+
+    progress_bar = RichProgressBar()
+    num_sanity_val_steps = 3
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        num_sanity_val_steps=num_sanity_val_steps,
+        limit_train_batches=1,
+        limit_val_batches=limit_val_batches,
+        max_epochs=1,
+        callbacks=progress_bar,
+    )
+
+    trainer.fit(model)
+    assert progress_bar.progress.tasks[0].completed == min(num_sanity_val_steps, limit_val_batches)