🛠️ Updated README for Your GitHub Repository

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🚀 Iterative Learning for Manipulation and Grasping Against Unknown Resistance Fields

Generalizable to Arbitrary Trajectories

This repository contains the implementation and supporting resources for the research paper:
"Iterative Learning for Manipulation and Grasping Against Unknown Resistance Fields that is Generalizable to Arbitrary Trajectories".

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📚 About the Project

This project presents a novel Iterative Learning Control (ILC) framework designed to enable robotic manipulators to adapt and generalize control strategies across unknown resistance fields and arbitrary trajectories.

Key contributions include:

• 📊 Direct and Safe Deployment: No prior training required in simulations or hardware setups.
• ⚡ Rapid Learning: Adaptation to new trajectories in just a few trials.
• 💻 Computational Simplicity: Optimized for edge-device deployments.
• 🤖 Generalization Framework: Transfer learned behaviors to new, unseen trajectories without extensive re-learning.

🔑 Core Components

1. Virtual System Integration: Maps nonlinear robot dynamics into a linear virtual system.
2. Generalizable Learning: Transfers learned control policies across arbitrary trajectories.
3. Trajectory Basis Learning: Builds foundational trajectory knowledge to enable generalization.
4. Safety Mechanisms: Prevents damage during unexpected resistance encounters.

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🤖 Applications

1. Material Cutting with Unknown Resistance:

   • Robot learns resistance characteristics of different materials (e.g., banana, cucumber).
   • Demonstrates safe adaptation and halting upon encountering unexpected resistance.

2. Object Grasping with Unknown Properties:

   • Grasps objects with unknown mass and inertia properties.
   • Transfers learned behaviors across arbitrary trajectories.

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📂 Repository Structure

ManipulatorX_ws/src
├── open_manipulator          # Submodule: OpenMANIPULATOR framework
├── open_manipulator_controls # Submodule: Control scripts for manipulator
├── open_manipulator_dependencies # Submodule: Dependencies for manipulator
├── open_manipulator_msgs     # Submodule: ROS message definitions
├── open_manipulator_simulations # Submodule: Simulation environments
├── Trial_Codes              # Experimental and test scripts
├── README.md                # This file
└── CMakeLists.txt           # Build configuration

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🛠️ Setup Instructions

1. Clone the Repository:

   git clone --recurse-submodules https://github.com/Debojit-D/ILC-for-Manipulation-and-Grasping.git
   cd ILC-for-Manipulation-and-Grasping

2. Install Dependencies:

   sudo apt-get install ros-noetic-desktop-full
   pip install -r requirements.txt

3. Build the Workspace:

   cd ManipulatorX_ws
   catkin_make
   source devel/setup.bash

4. Run Simulation/Experiments:

   roslaunch open_manipulator_simulations simulation.launch

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📊 Key Results

• ✅ Rapid adaptation across new trajectories within 3–4 trials.
• ✅ Stable grasp during object manipulation tasks.
• ✅ Demonstrated safety during unexpected resistance encounters.
• ✅ Efficient deployment on edge devices.

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📖 Reference Paper

Title: Iterative Learning for Manipulation and Grasping Against Unknown Resistance Fields that is Generalizable to Arbitrary Trajectories
Authors: Barat S., Suyash Patidar, Debojit Das, Shreyas Kumar, Shail Jadav, Harish J. Palanthandalam Madapusi
Affiliation: IIT Gandhinagar Robotics Laboratory

📑 Read the Full Paper (PDF)

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🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request to contribute to this project.

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📫 Contact

• Debojit Das
• 📧 [email protected]
• LinkedIn | Twitter

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📝 Acknowledgements

This work is supported by:

• Science and Engineering Research Board (SERB) under grant numbers CRG/2022/005196 and MTR/2021/000225.
• Prime Minister’s Research Fellowship (PMRF).

Special thanks to IIT Gandhinagar Robotics Laboratory for providing computational and research support.

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Let me know if you'd like further tweaks or additional sections! 🚀