RoboCore: Unified High-Throughput Robotics Library

Developed by Synria Robotics Co., Ltd. 🤖

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🔥 Features & Roadmap

Module	Features	Status
Kinematics	Forward Kinematics (NumPy/PyTorch Batch)	✅
	Inverse Kinematics (NumPy/PyTorch Batch)	✅
	Jacobian (NumPy/PyTorch Batch)	✅
	Bimanual FK (indep/relative/mirror)	✅
	Bimanual IK (indep/relative/mirror)	✅
	Bimanual Jacobian (indep/relative)	✅
Modeling	URDF parsing	✅
	MJCF parsing	✅
	Robot model abstraction	✅
	Multi-chain support	✅
	Bimanual robot model	✅
	Workspace analysis	✅
Transform	SE(3) operations	✅
	SO(3) operations	✅
	Rotation conversions	✅
	Quaternion operations	✅
Planning	Joint space (polynomial/spline/multi-segment)	✅
	Cartesian space (linear/circular/spline)	✅
	Orientation planning (SLERP)	✅
	Velocity profiles (trapezoidal/S-curve)	✅
Control	Joint position controller (PD/PID)	✅
	Joint velocity controller	✅
	Joint trajectory tracking controller	✅
	Cartesian position controller	✅
	Cartesian velocity controller	✅
	Cartesian trajectory tracking controller	✅
	Computed torque controller	⚪
	Impedance controller	⚪
	MPC controller	⚪
Analysis	Workspace analysis	✅
	Singularity analysis	🟡
WDF	SDF (Signed Distance Field)	✅
	RDF (Relative Distance Field)	✅
	Visualization	✅
Config	YAML configuration	✅
	Config schemas	✅
Bridge	MuJoCo simulation bridge	✅
	Physics simulation & evaluation	✅
	Real robot bridge (partial)	🟡
Dynamics	Inverse dynamics	⚪
	Forward dynamics	⚪
	Mass matrix computation	⚪
	Coriolis & gravity computation	⚪
Collision	Mesh-based collision detection	⚪
	Distance computation	⚪
Path Planning	RRT/RRT* algorithms	⚪
	PRM algorithms	⚪
	Optimization-based planning	⚪

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 .
pip install -r requirements.txt

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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, 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
import robocore as rc

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

# Set global backend for GPU
rc.set_backend('torch', device='cuda')

# Batch FK on GPU
poses = robot.fk(
    torch.tensor(q_batch), 
    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

🏗️ 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}
}