Update README.md

README.md

@@ -6,117 +6,6 @@ tags:
 - robotics
 - motion planning
 ---
-# Neural MP
+# DRP
 
-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 [codebase](https://github.com/mihdalal/neuralmotionplanner) are released under an MIT License.
-
-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.
-- **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 read [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 (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",
-        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)
-```
+5M ckpt at 7k epochs (7M steps)