---
license: mit
language:
- en
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.

All Neural MP checkpoints, as well as our [training 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/).

## Model Summary
- **Developed by:** The Neural MP team consisting of researchers from Carnegie Mellon University.
- **Language(s) (NLP):** en
- **License:** MIT
- **Pretraining Dataset:** Coming soon
- **Repository:** [https://github.com/mihdalal/neuralmotionplanner](https://github.com/mihdalal/neuralmotionplanner)
- **Paper:** Coming soon
- **Project Page & Videos:** [https://mihdalal.github.io/neuralmotionplanner/](https://mihdalal.github.io/neuralmotionplanner/)

## Installation

Please check [here](https://github.com/mihdalal/neural_mp?tab=readme-ov-file#installation-instructions) for detailed instructions

## Usage

Neural MP model takes in 3D point cloud and start & goal angles of the Franka robot as input, and predict 7-DoF delta joint actions. We provide a wrapper class [NeuralMP](https://github.com/mihdalal/neural_mp/blob/master/neural_mp/real_utils/neural_motion_planner.py) for inference and deploy our model in the real world.

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))

```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",
        type=str,
        default="mihdalal/NeuralMP",
        help="hugging face url to load the neural_mp model",
    )
    parser.add_argument(
        "--cache-name",
        type=str,
        default="scene1_single_blcok",
        help="Specify the scene cache file with pcd and rgb data",
    )
    parser.add_argument(
        "--use-cache",
        action="store_true",
        help=("If set, will use pre-stored point clouds"),
    )
    parser.add_argument(
        "--debug-combined-pcd",
        action="store_true",
        help=("If set, will show visualization of the combined pcd"),
    )
    parser.add_argument(
        "--denoise-pcd",
        action="store_true",
        help=("If set, will apply denoising to the pcds"),
    )
    parser.add_argument(
        "--train-mode", action="store_true", help=("If set, will eval with policy in training mode")
    )
    parser.add_argument(
        "--tto", action="store_true", help=("If set, will apply test time optimization")
    )
    parser.add_argument(
        "--in-hand", action="store_true", help=("If set, will enable in hand mode for eval")
    )
    parser.add_argument(
        "--in-hand-params",
        nargs="+",
        type=float,
        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(
        env=env,
        model_url=args.mdl_url,
        train_mode=args.train_mode,
        in_hand=args.in_hand,
        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,
            goal_config=goal_config,
            points=points,
            colors=colors,
        )
    else:
        trajectory = neural_mp.motion_plan(
            start_config=start_config,
            goal_config=goal_config,
            points=points,
            colors=colors,
        )

    success, joint_error = neural_mp.execute_motion_plan(trajectory, speed=0.2)
```