Spot Mini Mini

Kinematics and Gait:

Pybullet Environment and body manipulation with leg IK from: https://www.researchgate.net/publication/320307716_Inverse_Kinematic_Analysis_Of_A_Quadruped_Robot

Open-Loop Gait using 12-Point Bezier Curves based on: https://dspace.mit.edu/handle/1721.1/98270

Forward and Lateral Motion:

Yaw logic based on 4-wheel steering car: http://www.inase.org/library/2014/santorini/bypaper/ROBCIRC/ROBCIRC-54.pdf

Controls

Assuming you have a Logitech Gamepad F310:

A: switch between stepping and RPY

X: E-STOP (engage and disengage)

Stepping Mode:

• Right Stick Up/Down: Step Length
• Right Stick Left/Right: Lateral Fraction
• Left Stick Up/Down: Robot Height
• Left Stick Left/Right: Yaw Rate
• Arrow Pad Up/Down (DISCRETE): Step Height
• Arrow Pad Left/Right (DISCRETE): Step Depth
• Bottom Right/Left Bumpers: Step Velocity (modulate)
• Top Right/Left Bumpers: reset all to default

Viewing Mode:

• Right Stick Up/Down: Pitch
• Right Stick Left/Right: Roll
• Left Stick Up/Down: Robot Height
• Left Stick Left/Right: Yaw

Changing Step Velocity while moving forward:

Changing Step Length while moving forward:

Yaw In Place: Slightly push the Right Stick forward while pushing the Left Stick maximally in either direction:

Reinforcement Learning

I've found that the Bezier Curve gait lends itself well to optimization via RL. Notice that the open-loop forward command drifts significantly over time (rougly 1m per 2m forward):

With a one-dimensional action space [Yaw Rate], and a 16-dimensional observation space [IMU Inputs (8), Leg Phases (4), Leg Contacts (4)], an Augmented Random Search agent (linear) was able to correct the trajectory after 299 epochs:

Here is the policy output for this demo. It's clearly biased on one end to account for Spot's drift:

How To Run

Dependencies

• ROS Melodic
• Gazebo
• Pytorch
• Pybullet
• Gym
• OpenCV

Control:

First, you're going to need a joystick (okay, not really, but it's more fun if you have one).

Setting Up The Joystick:

• Get Number (you will see something like jsX): ls /dev/input/
• Make available to ROS: sudo chmod a+rw /dev/input/jsX
• Make sure <param name="dev" type="string" value="/dev/input/jsX"/> matches your setup in the launchfile

Then simply: roslaunch mini_ros spot_move.launch

You can ignore this msg: [ERROR] [1591631380.406690714]: Couldn't open joystick force feedback! It just means your controller is missing some functionality, but this package doesn't use it.

Non-Joystick Use

If you don't have a joystick, go to spot_bullet/src and do ./env_tester.py. A Pybullet sim will open up for you with the same controls you would have on the joystick, except each is on its own scrollbar.

Using Different Terrain

Navigate to spotmicro/heightfield.py and take a look at useProgrammatic and useTerrainFromPNG (you can play around with the mesh scales for each) to experiment with different terrains. Make sure that the spotBezierEnv instance has height_field=True in env_tester.py and spot_pybullet_interface depending on whether you're using the joystick/ROS version. The same goes for the RL environments. Note: these were adapted from the pybullet source code.

useTerrainFromPNG

useProgrammatic

Reinforcement Learning

Go to spot_bullet/src and do ./spot_ars_eval.py. When prompted, enter 299. That's the best policy I have. Although, I have since modified the Bezier gait generator, so you might want to git revert to this commit: 96e2fb948947bcac2720e3ac01c65c19edbf308e.