RoboCore: Unified High-Throughput Robotics Library

Developed by Synria Robotics Co., Ltd. 🤖

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✨ Features

Module	Functionality	Status
Modeling	URDF/MJCF parsing, Robot model abstraction	✅ Stable
Forward Kinematics	NumPy/PyTorch backends, Batch processing	✅ Stable
Inverse Kinematics	DLS/Pinv/Transpose methods, Multi-start	✅ Stable
Jacobian	Analytic/Numeric/Autograd methods	✅ Stable
Transform	SE(3)/SO(3) operations, Conversions	✅ Stable
Analysis	Workspace/Singularity analysis	✅ Beta
Planning	Trajectory generation	🚧 Alpha
Visualization	Kinematic tree display	✅ Stable
Configuration	YAML-based config management	✅ Stable

Supported Robot Formats

• ✅ URDF (Unified Robot Description Format)
• ✅ MJCF (MuJoCo XML) - Subset implementation for serial chains

Backend Support

• ✅ NumPy - CPU-optimized, 50-100x faster than pure Python
• ✅ PyTorch - GPU acceleration for batch operations

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🚀 Performance Benchmarks

Test Platform: Intel i7-10700K, NVIDIA RTX 3080, 6-DOF Manipulator

Single Configuration

Operation	Pure Python	NumPy	Speedup
Forward Kinematics	2.5 ms	0.05 ms	50x
Inverse Kinematics	450 ms	5.6 ms	80x
Jacobian (Analytic)	3.2 ms	0.03 ms	107x
Jacobian (Numeric)	18 ms	0.35 ms	51x

Batch Processing (1000 configs)

Operation	NumPy (CPU)	PyTorch (GPU)	Speedup
Forward Kinematics	45 ms	3.2 ms	14x
Jacobian (Analytic)	28 ms	2.1 ms	13x

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📦 Installation

# Clone repository
git clone https://github.com/Synria-Robotics/RoboCore.git
cd RoboCore

# Install (development mode)
pip install -e .

# Optional: Install with PyTorch for GPU support
pip install torch torchvision

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🎯 Quick Start

Basic Example

from robocore.modeling import RobotModel

# Load robot (auto-detects URDF/MJCF)
robot = RobotModel("path/to/robot.urdf")

# Display model info
robot.summary(show_chain=True)
robot.print_tree()

# Forward Kinematics
q = [0.0] * robot.num_dof()
pose = robot.fk(q, backend='numpy', return_end=True)

# Inverse Kinematics
result = robot.ik(pose, q_initial=q, method='pinv')
print(f"Solution: {result['q']}, Success: {result['success']}")

# Jacobian
J = robot.jacobian(q, method='analytic')  # Shape: (6, dof)

Batch Processing (GPU)

import torch

# Generate random configurations
q_batch = robot.random_q_batch(batch_size=1000)

# Batch FK on GPU
poses = robot.fk(
    torch.tensor(q_batch), 
    backend='torch', 
    device='cuda',
    return_end=True
)

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📚 Examples

# Robot model loading and validation
python examples/modeling/demo_robot_model.py --validate --show-tree

# Forward/Inverse kinematics
python examples/kinematics/demo_fk.py
python examples/kinematics/demo_ik.py

# Jacobian computation
python examples/kinematics/demo_jacobian.py

# Workspace analysis
python examples/analysis/demo_workspace.py --samples 10000

# Performance benchmark
python examples/kinematics/benchmark.py

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🧪 Testing

# Run all tests
pytest

# Specific test suites
pytest test/unit/          # Unit tests
pytest test/integration/   # Integration tests

# With coverage report
pytest --cov=robocore --cov-report=html

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📋 TODO & Roadmap

High Priority 🔴

• Collision Detection - Mesh-based collision checking
• Path Planning - RRT/RRT*/PRM algorithms
• Dynamics - Inverse/Forward dynamics computation
• Control - PID, MPC, impedance controllers

Medium Priority 🟡

• Optimization - Further SIMD/vectorization improvements
• Mobile Manipulators - Support for mobile bases
• Multi-Arm Systems - Coordinated multi-robot control

Low Priority 🟢

• Visualization - 3D interactive visualization (PyBullet/MuJoCo)
• Documentation - API docs, tutorials, best practices

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🏗️ Project Structure

RoboCore/
├── robocore/
│   ├── modeling/          # Robot model abstraction & parsers
│   ├── kinematics/        # FK/IK/Jacobian solvers
│   ├── transform/         # SE(3)/SO(3) operations
│   ├── planning/          # Motion planning (WIP)
│   ├── analysis/          # Workspace/singularity analysis
│   ├── configs/           # Configuration management
│   └── utils/             # Backend abstraction, utilities
├── examples/              # Demo scripts
├── test/                  # Unit & integration tests
└── docs/                  # Documentation

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📄 License

GPL-3.0 License
Copyright © 2025 Synria Robotics Co., Ltd.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

See the LICENSE file for the full license text.

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📧 Contact

• Website: synriarobotics.ai
• Email: support@synriarobotics.ai

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📖 Citation

@software{robocore2025,
  title = {RoboCore: High-Performance Robotics Kinematics Library},
  author = {Synria Robotics Team},
  year = {2025},
  publisher = {Synria Robotics Co., Ltd.},
  url = {https://github.com/Synria-Robotics/RoboCore},
  version = {1.0.0}
}