✅Robot Simulation with PyBullet – Simulating myCobot’s movements in a virtual environment.
✅Imitation Learning Model Training – Teaching the robot to replicate predefined tasks.
✅Precision Manipulation Techniques – Implementing joint velocity control and inverse kinematics for accurate movement.
✅Real-Time Visualization – Observing and analyzing the robot’s actions in simulation.
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PyBullet-based Imitation Learning for myCobot 320 Robot Arm
In recent years, robot reinforcement learning has developed rapidly, with imitation learning emerging as a promising approach. This method enables robots to learn tasks by observing human demonstrations, allowing for more intuitive training, especially in complex environments. To help simplify the learning process, this guide will walk you through implementing imitation learning for the 6-DOF myCobot 320 M5Stack using PyBullet.
What is PyBullet used for?
PyBullet is a physics simulation library widely used in robotics for creating virtual environments. It provides tools to simulate robot physics and their interactions with the environment.
myCobot Control Using a Pre-Trained Imitation Learning Model
In this project, we will use a pre-trained imitation learning model to control myCobot in a simulated environment. We reference the GitHub open-source project Simple Imitation Learning, authored by Sicelukwanda Zwane, who provided technical support. Additionally, Geraud Nangue Tasse, a key supporter of the Robot Learning Workshop, has made significant contributions to this project.
Key Features & Implementation:
✅Robot Simulation with PyBullet – Simulating myCobot’s movements in a virtual environment.
✅Imitation Learning Model Training – Teaching the robot to replicate predefined tasks.
✅Precision Manipulation Techniques – Implementing joint velocity control and inverse kinematics for accurate movement.
✅Real-Time Visualization – Observing and analyzing the robot’s actions in simulation.
Project Setup: A Step-by-Step Guide
1. Setting Up the PyBullet Simulation
Firstly, we need to load the 6-axis robotic arm myCobot 320 in PyBullet and set up the physical simulation environment.
import pybullet as p
import pybullet_data as pd
import numpy as np
import time
# use PyBullet
client_id = p.connect(p.GUI)
p.setAdditionalSearchPath(pd.getDataPath())
p.setGravity(0, 0, -9.8)
# load 2 model a plane and a robot
plane_id = p.loadURDF("plane.urdf")
robot_id = p.loadURDF("mycobot_description/urdf/mycobot/mycobot_urdf.urdf", useFixedBase=True)
# set step
time_step = 1/240
p.setTimeStep(time_step)
Alternatively, you can simply test robotic arm simulation using the code written by Sicelukwanda Zwane.
pip install pybullet numpy
git clone https://github.com/Sicelukwanda/robot_learning_tutorial.gi
cd robot_learning_tutorial
python3 move_basic.py
2. Collect Demonstration Data
In imitation learning, we need to collect a large amount of demonstration data, including:
- State – Joint angle and velocity of the robotic arm.
- Action – he target joint angle or velocity of the robotic arm.
Here we use pybullet to obtain the status of the robotic arm and define some demonstration actions with simple code.
states = []
actions = []
for i in range(100):
joint_positions = [0, 0.3*np.sin(i/10), -np.pi/4, 0, np.pi/4, 0]
states.append(joint_positions)
actions.append(joint_positions) # In imitation learning, the ideal action is the correct state
p.setJointMotorControlArray(robot_id, range(6), p.POSITION_CONTROL, targetPositions=joint_positions)
p.stepSimulation()
time.sleep(time_step)
# save data
np.save("states.npy", np.array(states))
p.save("actions.npy", np.array(actions))
3. Training the Robot
We need to install PyTorch and start training with a neural network model.
import torch
import torch.nn as nn
import torch.optim as optim
# load
X_train = np.load("states.npy")
y_train = np.load("actions.npy")
# trans to PyTorch tensor
X_train = torch.tensor(X_train, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.float32)
# load a simple network
class ImitationNetwork(nn.Module):
def __init__(self, input_dim, output_dim):
super(ImitationNetwork, self).__init__()
self.model = nn.Sequential(
nn.Linear(input_dim, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, output_dim)
)
def forward(self, x):
return self.model(x)
model = ImitationNetwork(input_dim=6, output_dim=6)
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_fn = nn.MSELoss()
# start training
epochs = 100
for epoch in range(epochs):
optimizer.zero_grad()
output = model(X_train)
loss = loss_fn(output, y_train)
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")
# save
torch.save(model.state_dict(), "imitation_model.pth")
4. Test
# Load your model
model.load_state_dict(torch.load("imitation_model.pth"))
model.eval()
for i in range(100):
joint_state = np.load("states.npy")[i]
input_tensor = torch.tensor(joint_state, dtype=torch.float32).unsqueeze(0)
predicted_action = model(input_tensor).detach().numpy().flatten()
p.setJointMotorControlArray(robot_id, range(6), p.POSITION_CONTROL, targetPositions=predicted_action)
p.stepSimulation()
time.sleep(time_step)
Due to the motion of the robotic arm is generated using code rather than manually collected, it lacks authenticity and makes the learning results ideal. Therefore, in actual learning, myCobot 320 M5Stack 6 axis robot arm can be used to collect better motion data.
Summary
This guide demonstrates how to use PyBullet for imitation learning with myCobot robotic arms, including data collection and validation.
In practical applications, you can extend this tutorial by:
✅Using more advanced neural networks (such as LSTMs) to process time-series data.
✅Integrating reinforcement learning (RL) to enable robots to autonomously optimize their behavior.
✅Deploying the learned strategies to a real myCobot robotic arm.
You can try to modify the task objectives, such as allowing myCobot to perform more complex operations such as picking and placing. We're excited to see more users and makers exploring the myCobot series robots to create innovative projects and participate in our cases collection activity.
Schematics
Code: data_collection.pyPython
import pybullet as p
import pybullet_data as pd
import numpy as np
import time
# use PyBullet
client_id = p.connect(p.GUI)
p.setAdditionalSearchPath(pd.getDataPath())
p.setGravity(0, 0, -9.8)
# load 2 model a plane and a robot
plane_id = p.loadURDF("plane.urdf")
robot_id = p.loadURDF("mycobot_description/urdf/mycobot/mycobot_urdf.urdf", useFixedBase=True)
# set step
time_step = 1/240
p.setTimeStep(time_step)
states = []
actions = []
for i in range(100):
joint_positions = [0, 0.3*np.sin(i/10), -np.pi/4, 0, np.pi/4, 0]
states.append(joint_positions)
actions.append(joint_positions) # In imitation learning, the ideal action is the correct state
p.setJointMotorControlArray(robot_id, range(6), p.POSITION_CONTROL, targetPositions=joint_positions)
p.stepSimulation()
time.sleep(time_step)
# save data
np.save("states.npy", np.array(states))
p.save("actions.npy", np.array(actions))
