Skip to content

README.md

Latest commit

 

History

History
62 lines (42 loc) · 2.47 KB

README.md

File metadata and controls

62 lines (42 loc) · 2.47 KB
Raw

dl_grasping_loco_manipulation_2025

Deep learning pipeline for grasping with the Boston Dynamics Spot robot. Developed at the Mobile Robotics Group, EESC (São Carlos Engineering School).

The project covers simulation-based dataset generation, neural network training for grasp selection, and real robot deployment using Spot's arm and gripper camera.

Repository Structure

├── urdf/             # Robot description files (URDF, meshes)
├── scripts/          # Genesis simulation scripts
│   └── spot_gripper/ # Gripper grasping experiments, including normal-aligned grasping and D2NT validation
├── dataset/          # Dataset extraction notebooks and 3D bottle models
├── grasp_selection/  # Grasp selection neural network training
└── spot_deploy/      # Real robot deployment pipeline (YOLO + D2NT + GraspNN)
    ├── evaluation/   # Full integration pipeline
    ├── images/       # Camera data from Spot's gripper
    └── tuning/       # Parameter tuning scripts

Setup

Simulations run on Genesis. See the installation guide.

Using conda:

conda create -n genesis_env python=3.12
conda activate genesis_env
pip install genesis-world

Using pyenv:

pyenv virtualenv 3.12 genesis_env
pyenv activate genesis_env
pip install genesis-world

An NVIDIA GPU is recommended. Install CUDA drivers for better performance.

Real Robot

Deployment uses the Boston Dynamics Spot SDK:

Normal maps on the real robot are generated using D2NT. In simulation, Genesis provides native tools for this.

Genesis Documentation

Contributing

Open an issue or pull request if you have suggestions or improvements.