add the get_started tutorial mentioned in paper

Author: Inoriros

.DS_Store

@@ -0,0 +1,114 @@
+"""
+Get Started Tutorial
+==================
+
+"""
+
+######################################################################
+# Uncomment this if you're using google colab to run this script
+#
+
+# !pip install pypose 
+
+
+######################################################################
+
+
+
+
+######################################################################
+# Sample Code of LieTensor
+# ---------------------------------------
+# The following code sample shows how to rotate random 
+# points and compute the gradient of batched rotation.
+#
+
+import torch
+import pypose as pp
+
+
+######################################################################
+# Create a random so(3) LieTensor
+# 
+
+r = pp.randn_so3(2, requires_grad=True)
+print(r)
+
+
+######################################################################
+# Get the Lie Group of the Lie Algebra
+# 
+
+R = r.Exp() # Equivalent to: R = pp.Exp(r)
+print(R)
+
+
+######################################################################
+# Create a random point and rotate it based on the Lie Group rotation tensor
+# 
+
+p = R @ torch.randn(3) # Rotate random point
+print(p)
+
+
+######################################################################
+# Compute the gradient and print it
+# 
+
+p.sum().backward() # Compute gradient
+r.grad # Print gradient
+
+
+######################################################################
+# Sample code of optimizer
+# ---------------------------------------------
+# We show how to estimate batched transform inverse by a
+# 2nd-order optimizer. Two usage options for a scheduler
+# are provided, each of which can work independently.
+# 
+
+from torch import nn
+import torch, pypose as pp
+from pypose.optim import LM
+from pypose.optim.strategy import Constant
+from pypose.optim.scheduler \
+import StopOnPlateau
+
+class InvNet(nn.Module):
+
+    def __init__(self, *dim):
+        super().__init__()
+        init = pp.randn_SE3(*dim)
+        self.pose = pp.Parameter(init)
+
+    def forward(self, input):
+        error = (self.pose @ input).Log()
+        return error.tensor()
+    
+device = torch.device("cuda")
+input = pp.randn_SE3(2, 2, device=device)
+invnet = InvNet(2, 2).to(device)
+strategy = Constant(damping=1e-4)
+optimizer = LM(invnet, strategy=strategy)
+scheduler = StopOnPlateau(optimizer,
+                          steps=10,
+                          patience=3,
+                          decreasing=1e-3,
+                          verbose=True)
+
+# 1st option, full optimization
+scheduler.optimize(input=input)
+
+# 2nd option, step optimization
+while scheduler.continual():
+    loss = optimizer.step(input)
+    scheduler.step(loss)
+
+
+
+######################################################################
+# And then we are finished with the two sample codes mentioned in our paper.
+# 
+# Now you may be free to explore other tutorials. 
+# See How PyPose can be utilized in real robotics applications.
+# 

beginner_source/get_started_tutorial.py

@@ -11,6 +11,7 @@ Welcome to Pypose Tutorials
    :caption: Introduction to PyPose
 
    beginner/lietensor_tutorial
+   beginner/get_started_tutorial
 
 .. toctree::
    :maxdepth: 2