Update demo docs w/ teleop usage info

docs/demos.md

@@ -99,9 +99,7 @@ The `demo_device_control.py` scripts shows how to teleoperate robot with [contro
 
 * **Keyboard**
     We use the keyboard to control the end-effector of the robot.
-    The keyboard provides 6-DoF control commands through various keys.
-    The commands are mapped to joint velocities through an inverse kinematics
-    solver from Bullet physics.
+    The keyboard provides 6-DoF control commands through various keyboard keys.
 
     **Note:**
         To run this script with macOS, you must run it with root access.
@@ -123,8 +121,14 @@ The `demo_device_control.py` scripts shows how to teleoperate robot with [contro
         This current implementation only supports macOS (Linux support can be added).
         Download and install the [driver](https://www.3dconnexion.com/service/drivers.html) before running the script.
 
+* **Mujoco GUI**
+        The Mujoco GUI provides a graphical user interface for viewing and interacting with a mujoco simulation. We use the GUI and a mouse to drag and drop mocap bodies, whose
+        poses are tracked by a controller. More specifically, once the mujoco GUI is loaded from running `python demo_device_control.py`, you first need to hit the <Tab> key to reach the interactive mujoco viewer state. Then, you should double click on
+        a mocap body. Finally, to drag the mocap body, you can hit to <Ctrl> or <Shift> key to translate or rotate the mocap body. For Mac users, you need to use `mjpython demo_device_control.py`. See the note from [mujoco](https://mujoco.readthedocs.io/en/stable/python.html#passive-viewer) for more details.
+
+
 Additionally, `--pos_sensitivity` and `--rot_sensitivity` provide relative gains for increasing / decreasing the user input
-device sensitivity. The `--controller` argument determines the choice of using either inverse kinematics controller (`ik`) or operational space controller (`osc`). The main difference is that user inputs with `ik`'s rotations are always taken relative to eef coordinate frame, whereas user inputs with `osc`'s rotations are taken relative to global frame (i.e., static / camera frame of reference). `osc` also tends to be more computationally efficient since `ik` relies on the backend [mink](https://github.com/kevinzakka/mink) IK solver.
+device sensitivity.
 
 
 Furthermore, please choose environment specifics with the following arguments:
@@ -160,18 +164,16 @@ Furthermore, please choose environment specifics with the following arguments:
 Examples:
 * For normal single-arm environment:
 ```
-$ python demo_device_control.py --environment PickPlaceCan --robots Sawyer --controller osc
+$ python demo_device_control.py --environment PickPlaceCan --robots Sawyer
 ```
 * For two-arm bimanual environment:
 ```
-$ python demo_device_control.py --environment TwoArmLift --robots Baxter --config bimanual --arm left --controller osc
+$ python demo_device_control.py --environment TwoArmLift --robots Baxter --config bimanual --arm left
 ```
 * For two-arm multi single-arm robot environment:
 ```
-$ python demo_device_control.py --environment TwoArmLift --robots Sawyer Sawyer --config parallel --controller osc
+$ python demo_device_control.py --environment TwoArmLift --robots Sawyer Sawyer --config parallel
 ```
-In **robosuite**, we use this teleoperation script extensively for debugging environment designs, tuning reward functions, and collecting human demonstration data.
-
 
 ### Video Recording
 The `demo_video_recording.py` script shows how to record a video of robot roll-out with the `imageio` library. This script uses offscreen rendering. This is useful for generating qualitative videos of robot policy behaviors. The generated video is in the mp4 format. Example: