newton-physics/newton
May 2025 – Aug 2025
3 Months active
Languages Used
C++PythonCUDA
Technical Skills
Algorithm ImplementationC++Code OrganizationCollision DetectionGeometryNumerical Integration
dylanturpin
PROFILE
Dylanturpin
Dylan Turpin contributed to the newton-physics/newton repository by developing advanced features for physics simulation and robotics tooling. He implemented articulated rigid body solvers and enhanced collision detection, improving simulation fidelity and debugging clarity. Using C++, Python, and CUDA, Dylan expanded the inverse kinematics module to support all joint types and introduced a floating-base option for robot models, accompanied by comprehensive refactoring and testing. He also modernized the codebase by updating Python version requirements and type-checking practices. His work included interactive 3D manipulation gizmos, robust benchmarking tools, and platform compatibility improvements, resulting in a maintainable, high-quality simulation framework.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
6
Lines of code
9,865
Activity Months3
Your Network
27 people
Work History
August 2025
2 Commits • 2 Features
Aug 1, 20252025-08 monthly summary for newton-physics/newton. Key accomplishments include delivering expanded inverse kinematics capabilities and platform modernization. Specifically, switched IK solver to velocity-space formulation to support all joint types, added a floating-base option for robot models, and performed extensive refactoring and testing. Upgraded platform compatibility by removing Python 3.9 support and raising the minimum Python to 3.10 across pyproject.toml and uv.lock, while adopting a pipe operator for type checks. These changes improve hardware compatibility, code quality, and future maintainability. Overall impact: broader hardware support, improved stability, easier onboarding for contributors, and a leaner, type-safe codebase.
2 Commits • 2 Features
Aug 1, 20252025-08 monthly summary for newton-physics/newton. Key accomplishments include delivering expanded inverse kinematics capabilities and platform modernization. Specifically, switched IK solver to velocity-space formulation to support all joint types, added a floating-base option for robot models, and performed extensive refactoring and testing. Upgraded platform compatibility by removing Python 3.9 support and raising the minimum Python to 3.10 across pyproject.toml and uv.lock, while adopting a pipe operator for type checks. These changes improve hardware compatibility, code quality, and future maintainability. Overall impact: broader hardware support, improved stability, easier onboarding for contributors, and a leaner, type-safe codebase.
August 2025
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 performance highlights for newton-physics/newton: Delivered two major features enhancing physics realism, tooling, and developer productivity. IK Module with Benchmarking and Interactive Playground provides a robust inverse kinematics solver with multiple Jacobian methods, integrated performance analysis, and a real-time playground for experimentation and benchmarking. Interactive 3D Manipulation Gizmo Toolkit adds a comprehensive gizmo.py with helper classes for 3D arrows/arcs, including collision detection, rendering, and intuitive drag/rotate interactions to improve visualization and manipulation workflows within the engine. A follow-up fix ensures gizmo.py helper availability to maintain tooling reliability.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 performance highlights for newton-physics/newton: Delivered two major features enhancing physics realism, tooling, and developer productivity. IK Module with Benchmarking and Interactive Playground provides a robust inverse kinematics solver with multiple Jacobian methods, integrated performance analysis, and a real-time playground for experimentation and benchmarking. Interactive 3D Manipulation Gizmo Toolkit adds a comprehensive gizmo.py with helper classes for 3D arrows/arcs, including collision detection, rendering, and intuitive drag/rotate interactions to improve visualization and manipulation workflows within the engine. A follow-up fix ensures gizmo.py helper availability to maintain tooling reliability.
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 performance summary for newton-physics/newton: Delivered key physics engine enhancements, visualization improvements, and robust collision detection, driving higher fidelity simulations and faster debugging. Implemented FeatherstoneSolver and SemiImplicitSolver with API alignment across the engine, migrated relevant helpers to the euler_solver module, and updated licensing and usage notes to reflect the changes. Enhanced OpenGL cloth self-contact visualization by hiding the ground plane and rendering in wireframe by default for clearer inspection. Fixed collision detection accuracy by incorporating radii for shapes (sphere, capsule, cylinder, cone) and accounting for thickness in penetration depth and total separation calculations. These efforts collectively improve simulation fidelity, developer productivity, and maintainability, delivering tangible business value.
4 Commits • 2 Features
May 1, 2025May 2025 performance summary for newton-physics/newton: Delivered key physics engine enhancements, visualization improvements, and robust collision detection, driving higher fidelity simulations and faster debugging. Implemented FeatherstoneSolver and SemiImplicitSolver with API alignment across the engine, migrated relevant helpers to the euler_solver module, and updated licensing and usage notes to reflect the changes. Enhanced OpenGL cloth self-contact visualization by hiding the ground plane and rendering in wireframe by default for clearer inspection. Fixed collision detection accuracy by incorporating radii for shapes (sphere, capsule, cylinder, cone) and accounting for thickness in penetration depth and total separation calculations. These efforts collectively improve simulation fidelity, developer productivity, and maintainability, delivering tangible business value.
May 2025
Activity
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Quality Metrics
Correctness92.6%
Maintainability88.8%
Architecture91.2%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
3D GraphicsAlgorithm ImplementationC++CUDACode OrganizationCode RefactoringCollision DetectionDependency ManagementGPU ComputingGPU ProgrammingGeometryInteractive GizmosInverse KinematicsNumerical IntegrationNumerical Optimization
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