Update README.md

README.md

@@ -6,14 +6,13 @@ tags:
 - robotics
 - motion planning
 ---
-
 # Neural MP
 
-Neural MP is a machine learning-based motion planning system for robotic manipulation tasks. It combines neural networks trained on large-scale simulated data with lightweight optimization techniques to generate efficient, collision-free trajectories. Neural MP is designed to generalize across diverse environments and obstacle configurations, making it suitable for both simulated and real-world robotic applications. This repository contains the implementation, data generation tools, and evaluation scripts for Neural MP.
+Neural MP is a machine learning-based motion planning system for robotic manipulation tasks. It combines neural networks trained on large-scale simulated data with lightweight optimization techniques to generate efficient, collision-free trajectories. Neural MP is designed to generalize across diverse environments and obstacle configurations, making it suitable for both simulated and real-world robotic applications. This repository contains the model weights for Neural MP.
 
-All Neural MP checkpoints, as well as our [training codebase](https://github.com/mihdalal/neuralmotionplanner) are released under an MIT License.
+All Neural MP checkpoints, as well as our [codebase](https://github.com/mihdalal/neuralmotionplanner) are released under an MIT License.
 
-For full details, please read our paper(coming soon) and see [our project page](https://mihdalal.github.io/neuralmotionplanner/).
+For full details, please read our [paper](https://mihdalal.github.io/neuralmotionplanner/resources/paper.pdf) and see [our project page](https://mihdalal.github.io/neuralmotionplanner/).
 
 ## Model Summary
 - **Developed by:** The Neural MP team consisting of researchers from Carnegie Mellon University.
@@ -26,7 +25,7 @@ For full details, please read our paper(coming soon) and see [our project page](
 
 ## Installation
 
-Please check [here](https://github.com/mihdalal/neural_mp?tab=readme-ov-file#installation-instructions) for detailed instructions
+Please read [here](https://github.com/mihdalal/neural_mp?tab=readme-ov-file#installation-instructions) for detailed instructions
 
 ## Usage
 
@@ -34,17 +33,14 @@ Neural MP model takes in 3D point cloud and start & goal angles of the Franka ro
 
 Here's an deployment example with the Manimo Franka control library:
 
-Note: using Manimo is not required, you may use other Franka control libraries by creating a wrapper class which inherits from FrankaRealEnv (check [franka_real_env.py](https://github.com/mihdalal/neural_mp/blob/master/neural_mp/envs/franka_real_env.py))
+Note: using Manimo is not required, you may use other Franka control libraries by creating a wrapper class which inherits from FrankaRealEnv (see [franka_real_env.py](https://github.com/mihdalal/neural_mp/blob/master/neural_mp/envs/franka_real_env.py))
 
 ```python
 import argparse
 import numpy as np
-
 from neural_mp.envs.franka_real_env import FrankaRealEnvManimo
 from neural_mp.real_utils.neural_motion_planner import NeuralMP
-
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--mdl_url",
@@ -89,7 +85,6 @@ if __name__ == "__main__":
         default=[0.1, 0.1, 0.1, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 1.0],
         help="Specify the bounding box of the in hand object. 10 params in total [size(xyz), pos(xyz), ori(xyzw)] 3+3+4.",
     )
-
     args = parser.parse_args()
     env = FrankaRealEnvManimo()
     neural_mp = NeuralMP(
@@ -100,18 +95,15 @@ if __name__ == "__main__":
         in_hand_params=args.in_hand_params,
         visualize=True,
     )
-
     points, colors = neural_mp.get_scene_pcd(
         use_cache=args.use_cache,
         cache_name=args.cache_name,
         debug_combined_pcd=args.debug_combined_pcd,
         denoise=args.denoise_pcd,
     )
-
     # specify start and goal configurations
     start_config = np.array([-0.538, 0.628, -0.061, -1.750, 0.126, 2.418, 1.610])
     goal_config = np.array([1.067, 0.847, -0.591, -1.627, 0.623, 2.295, 2.580])
-
     if args.tto:
         trajectory = neural_mp.motion_plan_with_tto(
             start_config=start_config,
@@ -126,6 +118,5 @@ if __name__ == "__main__":
             points=points,
             colors=colors,
         )
-
     success, joint_error = neural_mp.execute_motion_plan(trajectory, speed=0.2)
-```
+```