diff --git a/rl/sims/gym_wrappers.py b/rl/sims/gym_wrappers.py
new file mode 100644
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+++ b/rl/sims/gym_wrappers.py
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+from functools import partial
+
+import numpy as np
+
+import gym
+import pydart2 as pydart
+from gym.envs.dart import DartEnv
+# Copyright (c) 2020 Georgia Tech Robot Learning Lab
+# Licensed under the MIT License.
+
+from gym.spaces import Box
+from gym.wrappers import TimeLimit
+from rl.core.function_approximators.supervised_learners import SupervisedLearner
+
+""" 
+Extend dart envs using gym wrappers. 
+
+Example:
+
+from rl.sims.gym_wrappers import create_dartenv
+
+env = create_dartenv(gym_id, seed, **sim_kwargs)
+
+The current implementation has the following limitations:
+- The wrappers can not be used to wrap an env in arbitrary order. 
+- Its compatibility with parallelism needs investigation. 
+"""
+
+
+def _t_state(t, horizon):
+    return t / horizon
+
+# The main entrance. 
+def create_dartenv(gym_kwargs, seed=None, use_time_info=False, bias=None, 
+                   dyn_sup=None):
+    env = gym.make(**gym_kwargs)
+    env.seed(seed)
+    t_state = partial(_t_state, horizon=env.spec.max_episode_steps)
+    env = AugmentDartEnv(env, bias=bias) if bias else AugmentDartEnv(env)
+    env = LearnDyn(env, dyn_sup=dyn_sup) if dyn_sup else env
+    env = ObWithTime(env, t_state) if use_time_info else env
+    return env
+
+
+class Wrapper(gym.Wrapper):
+    # Patch for gym.
+    # Currently, only public class method can be accessed, defined 
+    # in __getattr__ method of gym Wrapper class.
+    def getattr_protected(self, cls, name):
+        assert name.startswith('_')
+        env = self.get_class(cls)
+        return getattr(env, name)
+
+    def setattr(self, cls, name, value):
+        env = self.get_class(cls)
+        setattr(env, name, value)
+
+    def get_class(self, cls):
+        env = self
+        try:
+            while not isinstance(env, cls):
+                env = env.env
+        except:
+            raise ValueError('env is not in class: {}'.format(cls))
+        return env
+
+    def is_class(self, cls):
+        try:
+            self.get_class(cls)
+        except ValueError:
+            return False
+        return True
+
+    def assert_class(self, cls):
+        self.get_class(cls)
+
+
+class ObWithTime(Wrapper):
+    def __init__(self, env, t_state, t_low=0.0, t_high=1.0):
+        # `t_state`: a function that maps time to desired features
+        # t_low, t_high: limits of the t state.
+        super().__init__(env)
+        # Change the observation space.
+        assert isinstance(self.observation_space, Box)
+        low, high = self.observation_space.low, self.observation_space.high
+        assert len(low.shape) == len(high.shape) == 1
+        low, high = np.hstack([low, t_low]), np.hstack([high, t_high])
+        self.observation_space = Box(low, high)
+        self.t_state = t_state
+
+    def append_ob(self, ob):
+        t = self.getattr_protected(TimeLimit, '_elapsed_steps')
+        return np.concatenate([ob.flatten(), (self.t_state(t),)])
+
+    def reset(self, **kwargs):
+        ob = self.env.reset(**kwargs)
+        return self.append_ob(ob)
+
+    def step(self, action):
+        res = list(self.env.step(action))
+        res[0] = self.append_ob(res[0])
+        return tuple(res)
+
+
+class AugmentDartEnv(Wrapper):
+    # Augmented DartEnv with commonly used extensions. 
+    def __init__(self, env, bias=None):
+        # if bias is .0 or None, no perturbation of the physical parameters will be added. 
+        super().__init__(env)
+        self.assert_class(DartEnv)
+        self.bias = bias
+        if not (bias is None or np.isclose(self.bias, 0.0)):
+            self._perturb_physcial_params(bias)
+        self.get_obs = self.getattr_protected(DartEnv, '_get_obs')
+
+    @property
+    def state(self):
+        return self.state_vector()
+
+    def reset(self, state=None, tm=None):
+        ob = self.env.reset()
+        if state is not None:
+            self.set_state_vector(state)
+            ob = self.get_obs()
+        if tm is not None:
+            self.setattr(TimeLimit, '_elapsed_steps', tm)
+        return ob
+
+    def _perturb_physcial_params(self, bias):
+        if bias is None or np.isclose(bias, 0.0):
+            return
+        # Mass.
+        for body in self.robot_skeleton.bodynodes:
+            body.set_mass(body.m * self._rand_ratio(bias, self.np_random))
+        # Damping coeff for revolute joints.
+        for j in self.robot_skeleton.joints:
+            if isinstance(j, pydart.joint.RevoluteJoint):
+                coeff = j.damping_coefficient(0) * self._rand_ratio(bias, self.np_random)
+                j.set_damping_coefficient(0, coeff)
+
+    @staticmethod
+    def _rand_ratio(bias, np_rand):
+        """Helper function to be used in _perturb_physcial_params."""
+        assert 1.0 > bias >= 0.0
+        return 1.0 + bias * (np_rand.choice(2) * 2.0 - 1.0)
+
+
+class LearnDyn(Wrapper):
+    # Currently only works for DartEnv, due to the access to get_obs method.
+    def __init__(self, env, dyn_sup):
+        super().__init__(env)
+        assert isinstance(env, DartEnv)
+        assert isinstance(dyn_sup, SupervisedLearner)
+        self.dyn_sup = dyn_sup  # predicts next state given current state and action
+
+    def step(self, action):
+        # Assume rw is a function of st and ac.
+        _, rw, dn, info = self.env.step(action)
+        st = self.dyn_sup(np.hstack([self.state, action]))
+        self.set_state_vector(st)
+        return self.get_obs(), rw, dn, info