diff --git a/android_env/wrappers/swipe_action_wrapper.py b/android_env/wrappers/swipe_action_wrapper.py
new file mode 100644
index 0000000..1faefc1
--- /dev/null
+++ b/android_env/wrappers/swipe_action_wrapper.py
@@ -0,0 +1,146 @@
+# coding=utf-8
+# Copyright 2026 DeepMind Technologies Limited.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Wraps the AndroidEnv environment to provide swipe actions."""
+
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
+
+from android_env import env_interface
+from android_env.components import action_type
+from android_env.wrappers import base_wrapper
+import dm_env
+from dm_env import specs
+import numpy as np
+
+
+class SwipeActionWrapper(base_wrapper.BaseWrapper):
+  """AndroidEnv with swipe actions.
+
+  Converts a single swipe action (start position, end position) into a sequence
+  of TOUCH steps with linearly interpolated positions, followed by a LIFT at the
+  end position.
+  """
+
+  def __init__(
+      self,
+      env: env_interface.AndroidEnvInterface,
+      *,
+      num_steps: int = 10,
+  ) -> None:
+    """Initializes the instance.
+
+    Args:
+      env: The underlying environment.
+      num_steps: The number of TOUCH steps used to interpolate between the start
+        and end positions.
+    """
+    super().__init__(env)
+    self._assert_base_env()
+    if num_steps < 1:
+      raise ValueError(f'num_steps must be >= 1, got {num_steps}.')
+    self._num_steps = num_steps
+    self._env_steps = 0
+    self._touch_position_spec = cast(
+        specs.BoundedArray, self._env.action_spec()['touch_position']
+    )
+    self._action_type_dtype = self._env.action_spec()['action_type'].dtype
+
+  def _assert_base_env(self) -> None:
+    parent_action_spec = self._env.action_spec()
+    assert len(parent_action_spec) == 2
+    assert not parent_action_spec['action_type'].shape
+    assert parent_action_spec['touch_position'].shape == (2,)
+
+  def stats(self) -> dict[str, Any]:
+    """Returns a dictionary of metrics logged by the environment."""
+    logs = self._env.stats()
+    logs.update({'env_steps': self._env_steps})
+    return logs
+
+  def _process_action(
+      self, action: Mapping[str, np.ndarray]
+  ) -> Sequence[dict[str, np.ndarray]]:
+    start = np.asarray(action['start_position'], dtype=np.float32)
+    end = np.asarray(action['end_position'], dtype=np.float32)
+    touch_dtype = self._touch_position_spec.dtype
+
+    alphas = np.linspace(0.0, 1.0, self._num_steps, dtype=np.float32)
+    positions = start + alphas[:, np.newaxis] * (end - start)
+
+    actions = []
+    for position in positions:
+      actions.append({
+          'action_type': np.array(action_type.ActionType.TOUCH).astype(
+              self._action_type_dtype
+          ),
+          'touch_position': position.astype(touch_dtype),
+      })
+
+    actions.append({
+        'action_type': np.array(action_type.ActionType.LIFT).astype(
+            self._action_type_dtype
+        ),
+        'touch_position': end.astype(touch_dtype),
+    })
+    return actions
+
+  def step(self, action: Mapping[str, np.ndarray]) -> dm_env.TimeStep:
+    """Takes a step in the environment."""
+    actions = self._process_action(action)
+    total_reward = 0.0
+    reward_discount = 1.0
+    step_type = dm_env.StepType.MID
+    discount = None
+    observation = None
+    for sub_action in actions:
+      step_type, reward, discount, observation = self._env.step(sub_action)
+      self._env_steps += 1
+      if reward is not None:
+        total_reward += reward_discount * reward
+      if discount is not None:
+        reward_discount *= discount
+      if step_type == dm_env.StepType.LAST:
+        return dm_env.TimeStep(
+            step_type=step_type,
+            reward=total_reward,
+            discount=discount,
+            observation=observation,
+        )
+    return dm_env.TimeStep(
+        step_type=step_type,
+        reward=total_reward,
+        discount=discount,
+        observation=observation,
+    )
+
+  def action_spec(self) -> dict[str, specs.Array]:
+    touch_spec = self._touch_position_spec
+    return {
+        'start_position': specs.BoundedArray(
+            shape=(2,),
+            dtype=touch_spec.dtype,
+            minimum=touch_spec.minimum,
+            maximum=touch_spec.maximum,
+            name='start_position',
+        ),
+        'end_position': specs.BoundedArray(
+            shape=(2,),
+            dtype=touch_spec.dtype,
+            minimum=touch_spec.minimum,
+            maximum=touch_spec.maximum,
+            name='end_position',
+        ),
+    }
diff --git a/android_env/wrappers/swipe_action_wrapper_test.py b/android_env/wrappers/swipe_action_wrapper_test.py
new file mode 100644
index 0000000..abe18b0
--- /dev/null
+++ b/android_env/wrappers/swipe_action_wrapper_test.py
@@ -0,0 +1,246 @@
+# coding=utf-8
+# Copyright 2026 DeepMind Technologies Limited.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from android_env import env_interface
+from android_env.components import action_type
+from android_env.wrappers import swipe_action_wrapper
+import dm_env
+from dm_env import specs
+import numpy as np
+
+
+def _make_array_spec(shape, dtype, name):
+  return specs.BoundedArray(
+      name=name,
+      shape=shape,
+      dtype=dtype,
+      minimum=np.zeros(shape),
+      maximum=np.ones(shape),
+  )
+
+
+class SwipeActionWrapperTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self._base_action_spec = {
+        'action_type': specs.DiscreteArray(num_values=3, name='action_type'),
+        'touch_position': _make_array_spec(
+            shape=(2,), dtype=np.float32, name='touch_position'
+        ),
+    }
+    self.base_env = mock.create_autospec(env_interface.AndroidEnvInterface)
+    self.base_env.action_spec.return_value = self._base_action_spec
+
+  def test_process_action_interpolation(self):
+    num_steps = 5
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=num_steps
+    )
+    start = np.array([0.0, 0.0], dtype=np.float32)
+    end = np.array([1.0, 1.0], dtype=np.float32)
+    action = {'start_position': start, 'end_position': end}
+
+    actions = wrapped_env._process_action(action)
+    self.assertLen(actions, num_steps + 1)
+
+    expected_alphas = [0.0, 0.25, 0.5, 0.75, 1.0]
+    for i, alpha in enumerate(expected_alphas):
+      expected_position = start * (1.0 - alpha) + end * alpha
+      np.testing.assert_allclose(
+          actions[i]['touch_position'], expected_position, rtol=1e-6
+      )
+      self.assertEqual(
+          actions[i]['action_type'], action_type.ActionType.TOUCH
+      )
+
+    self.assertEqual(actions[-1]['action_type'], action_type.ActionType.LIFT)
+    np.testing.assert_allclose(actions[-1]['touch_position'], end, rtol=1e-6)
+
+  def test_process_action_single_step(self):
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=1
+    )
+    start = np.array([0.2, 0.3], dtype=np.float32)
+    end = np.array([0.8, 0.9], dtype=np.float32)
+    actions = wrapped_env._process_action({
+        'start_position': start,
+        'end_position': end,
+    })
+
+    self.assertLen(actions, 2)
+    np.testing.assert_allclose(actions[0]['touch_position'], start, rtol=1e-6)
+    self.assertEqual(actions[0]['action_type'], action_type.ActionType.TOUCH)
+    np.testing.assert_allclose(actions[1]['touch_position'], end, rtol=1e-6)
+    self.assertEqual(actions[1]['action_type'], action_type.ActionType.LIFT)
+
+  def test_invalid_num_steps(self):
+    with self.assertRaisesRegex(ValueError, 'num_steps must be >= 1'):
+      swipe_action_wrapper.SwipeActionWrapper(self.base_env, num_steps=0)
+
+  def test_reset(self):
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=5
+    )
+    fake_timestep = 'ts'
+    self.base_env.reset.return_value = fake_timestep
+    ts = wrapped_env.reset()
+    self.base_env.reset.assert_called_once()
+    self.assertEqual(fake_timestep, ts)
+
+  def test_step(self):
+    num_steps = 5
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=num_steps
+    )
+    fake_timestep = dm_env.TimeStep(
+        step_type='fake_type', reward=0.0, discount=1.0, observation='fake_obs'
+    )
+    self.base_env.step.return_value = fake_timestep
+    self.base_env.stats.return_value = {}
+
+    ts = wrapped_env.step({
+        'start_position': np.array([0.0, 0.0], dtype=np.float32),
+        'end_position': np.array([1.0, 1.0], dtype=np.float32),
+    })
+    stats = wrapped_env.stats()
+
+    self.assertEqual(num_steps + 1, self.base_env.step.call_count)
+    self.assertIsInstance(ts, dm_env.TimeStep)
+    self.assertIsInstance(stats, dict)
+    self.assertIn('env_steps', stats)
+    self.assertEqual(stats['env_steps'], num_steps + 1)
+
+  def test_step_terminal(self):
+    num_steps = 5
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=num_steps
+    )
+    normal_timestep = dm_env.TimeStep(
+        step_type=dm_env.StepType.MID,
+        reward=0.0,
+        discount=1.0,
+        observation='fake_obs',
+    )
+    terminal_timestep = dm_env.TimeStep(
+        step_type=dm_env.StepType.LAST,
+        reward=1.0,
+        discount=0.0,
+        observation='final_obs',
+    )
+    self.base_env.step.side_effect = [
+        normal_timestep,
+        normal_timestep,
+        terminal_timestep,
+    ]
+    self.base_env.stats.return_value = {}
+
+    ts = wrapped_env.step({
+        'start_position': np.array([0.0, 0.5], dtype=np.float32),
+        'end_position': np.array([1.0, 0.5], dtype=np.float32),
+    })
+
+    self.assertEqual(3, self.base_env.step.call_count)
+    self.assertIs(ts.step_type, dm_env.StepType.LAST)
+    self.assertEqual(ts.reward, 1.0)
+
+  def test_step_accumulates_reward(self):
+    num_steps = 3
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=num_steps
+    )
+    self.base_env.step.side_effect = [
+        dm_env.TimeStep(dm_env.StepType.MID, 0.1, 1.0, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 0.2, 1.0, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 0.3, 1.0, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 0.4, 1.0, 'obs'),
+    ]
+    self.base_env.stats.return_value = {}
+
+    ts = wrapped_env.step({
+        'start_position': np.array([0.0, 0.0], dtype=np.float32),
+        'end_position': np.array([1.0, 0.0], dtype=np.float32),
+    })
+
+    self.assertAlmostEqual(ts.reward, 1.0)
+
+  def test_step_accumulates_discounted_reward(self):
+    num_steps = 3
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=num_steps
+    )
+    self.base_env.step.side_effect = [
+        dm_env.TimeStep(dm_env.StepType.MID, 1.0, 0.5, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 1.0, 0.5, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 1.0, 0.5, 'obs'),
+        dm_env.TimeStep(dm_env.StepType.MID, 1.0, 0.5, 'obs'),
+    ]
+    self.base_env.stats.return_value = {}
+
+    ts = wrapped_env.step({
+        'start_position': np.array([0.0, 0.0], dtype=np.float32),
+        'end_position': np.array([1.0, 0.0], dtype=np.float32),
+    })
+
+    # 1.0 + 0.5 + 0.25 + 0.125 = 1.875
+    self.assertAlmostEqual(ts.reward, 1.875)
+
+  def test_observation_spec(self):
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=5
+    )
+    fake_obs_spec = 'fake_obs_spec'
+    self.base_env.observation_spec.return_value = fake_obs_spec
+    observation_spec = wrapped_env.observation_spec()
+    self.base_env.observation_spec.assert_called_once()
+    self.assertEqual(fake_obs_spec, observation_spec)
+
+  def test_action_spec(self):
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=5
+    )
+    action_spec = wrapped_env.action_spec()
+    self.assertCountEqual(
+        action_spec.keys(), ['start_position', 'end_position']
+    )
+    for key in ('start_position', 'end_position'):
+      spec = action_spec[key]
+      self.assertIsInstance(spec, specs.BoundedArray)
+      self.assertEqual(spec.shape, (2,))
+      self.assertEqual(spec.dtype, np.float32)
+
+  def test_stats(self):
+    self.base_env.stats.return_value = {
+        'some_key': 12345,
+        'another_key': 5.4321,
+    }
+    wrapped_env = swipe_action_wrapper.SwipeActionWrapper(
+        self.base_env, num_steps=5
+    )
+
+    stats = wrapped_env.stats()
+
+    self.assertIn('some_key', stats)
+    self.assertEqual(stats['some_key'], 12345)
+    self.assertIn('env_steps', stats)
+    self.assertEqual(stats['env_steps'], 0)
+
+
+if __name__ == '__main__':
+  absltest.main()