agimus-project/agimus_controller
Nov 2024 – Jul 2025
8 Months active
Languages Used
CMakeMarkdownPythonShellC++YAMLyamlXML
Technical Skills
Build System ConfigurationControl SystemsDocumentationMPCOptimizationPython
Théo Martinez
PROFILE
Théo Martinez
Tomas Martinez-Fernandez developed advanced robot control and trajectory optimization features in the agimus_controller repository, focusing on modular MPC-based planning and robust ROS/ROS2 integration. He engineered a flexible trajectory framework with dynamic weighting, visual servoing, and collision-aware planning, leveraging Python, C++, and ROS to enable reliable end-effector control and real-time data visualization. His work included parameterized configuration, automated testing, and visualization utilities using Matplotlib and Meshcat, supporting both simulation and deployment. Through systematic code refactoring, documentation, and legacy code cleanup, Tomas improved maintainability and scalability, delivering a production-ready robotics control backbone with strong validation, observability, and extensibility.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
99Total
Bugs
7
Commits
99
Features
30
Lines of code
11,864
Activity Months8
Your Network
13 people
Work History
July 2025
10 Commits • 4 Features
Jul 1, 2025In July 2025, the agimus_controller team delivered feature-rich trajectory optimization improvements, enhanced visualization and analysis tooling, and essential codebase cleanup, delivering stronger state accuracy, safer planning, and reduced maintenance overhead. Key outcomes include improved robot model attachment handling and end-effector pose updates for more accurate state representation; dynamic collision avoidance weights configurable via new ROS parameters; Meshcat visualization for the panda pick-and-place task plus ROS bag plotting for MPC analysis and enhanced collision avoidance plots; and proactive maintenance removing deprecated files, updating documentation, and removing outdated configuration parameters.
10 Commits • 4 Features
Jul 1, 2025In July 2025, the agimus_controller team delivered feature-rich trajectory optimization improvements, enhanced visualization and analysis tooling, and essential codebase cleanup, delivering stronger state accuracy, safer planning, and reduced maintenance overhead. Key outcomes include improved robot model attachment handling and end-effector pose updates for more accurate state representation; dynamic collision avoidance weights configurable via new ROS parameters; Meshcat visualization for the panda pick-and-place task plus ROS bag plotting for MPC analysis and enhanced collision avoidance plots; and proactive maintenance removing deprecated files, updating documentation, and removing outdated configuration parameters.
July 2025
June 2025
8 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for agimus_controller: Delivered key MPC-integrated Panda trajectory planning overhaul, enhanced transform data handling, introduced MPC visualization utilities, and performed substantial internal refactors to HPP/Panda tooling. These changes advance reliability, observability, and modularity, translating into smoother robot operation, safer planning under dynamic conditions, and reduced maintenance burden.
June 2025
8 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for agimus_controller: Delivered key MPC-integrated Panda trajectory planning overhaul, enhanced transform data handling, introduced MPC visualization utilities, and performed substantial internal refactors to HPP/Panda tooling. These changes advance reliability, observability, and modularity, translating into smoother robot operation, safer planning under dynamic conditions, and reduced maintenance burden.
May 2025
8 Commits • 2 Features
May 1, 2025Month: 2025-05 – Key features delivered and impact in agimus_controller. This month delivered dynamic weighting in Cartesian-space trajectory generation and comprehensive visual servoing enhancements with time-based activation, dynamic weighting, and integration into the trajectory publishing flow. No major bugs reported. Business value: enables more dynamic and robust end-effector control, faster iteration, and clearer telemetry and documentation. Technologies/skills demonstrated: ROS-based trajectory planning, dynamic weighting algorithms, visual servoing, code refactoring, and documentation.
8 Commits • 2 Features
May 1, 2025Month: 2025-05 – Key features delivered and impact in agimus_controller. This month delivered dynamic weighting in Cartesian-space trajectory generation and comprehensive visual servoing enhancements with time-based activation, dynamic weighting, and integration into the trajectory publishing flow. No major bugs reported. Business value: enables more dynamic and robust end-effector control, faster iteration, and clearer telemetry and documentation. Technologies/skills demonstrated: ROS-based trajectory planning, dynamic weighting algorithms, visual servoing, code refactoring, and documentation.
May 2025
April 2025
21 Commits • 4 Features
Apr 1, 2025Concise monthly summary for April 2025 focusing on business value and technical achievements in the agimus_controller repository.
April 2025
21 Commits • 4 Features
Apr 1, 2025Concise monthly summary for April 2025 focusing on business value and technical achievements in the agimus_controller repository.
March 2025
22 Commits • 8 Features
Mar 1, 2025March 2025: Delivered major robustness and value to agimus_controller through enhanced robot model loading, MPC data workflows, and expanded testing coverage. Implementations include environment- and collision-aware robot models with SRDF topic loading, robust MPC debug data support, offline MPC utilities, and strengthened data handling and test validation, resulting in faster integration, more reliable simulations, and clearer business value.
22 Commits • 8 Features
Mar 1, 2025March 2025: Delivered major robustness and value to agimus_controller through enhanced robot model loading, MPC data workflows, and expanded testing coverage. Implementations include environment- and collision-aware robot models with SRDF topic loading, robust MPC debug data support, offline MPC utilities, and strengthened data handling and test validation, resulting in faster integration, more reliable simulations, and clearer business value.
March 2025
January 2025
13 Commits • 5 Features
Jan 1, 2025January 2025 (2025-01): Focused on delivering MPC-enabled control path in agimus_controller, hardening ROS control reliability, and establishing validation and governance foundations. Key outcomes include MPC integration with new YAML config and initialization, parameter/type and message handling fixes, licensing updates, performance and maintainability improvements, and new tooling for model sensibilities and control data validation. Result: clearer business value with faster MPC workflows, more robust runtime, and improved open-source readiness.
January 2025
13 Commits • 5 Features
Jan 1, 2025January 2025 (2025-01): Focused on delivering MPC-enabled control path in agimus_controller, hardening ROS control reliability, and establishing validation and governance foundations. Key outcomes include MPC integration with new YAML config and initialization, parameter/type and message handling fixes, licensing updates, performance and maintainability improvements, and new tooling for model sensibilities and control data validation. Result: clearer business value with faster MPC workflows, more robust runtime, and improved open-source readiness.
December 2024
11 Commits • 2 Features
Dec 1, 2024December 2024 - AgimusController (agimus_project/agimus_controller): Progress on ROS2 migration with unified parameter management, MPC enhancements, and code hygiene. Delivered foundational ROS2 migration and parameter library integration; introduced YAML-based parameter loading, collision/visual geometry support, and timer-driven execution for the MPC node; fixed critical MPC frame naming and duration conversions; removed deprecated files; improved documentation and dependency handling. Business value: prepared the controller for ROS2 production deployments with configurable, maintainable parameters; improved runtime reliability and modularity; reduced risk and maintenance overhead.
11 Commits • 2 Features
Dec 1, 2024December 2024 - AgimusController (agimus_project/agimus_controller): Progress on ROS2 migration with unified parameter management, MPC enhancements, and code hygiene. Delivered foundational ROS2 migration and parameter library integration; introduced YAML-based parameter loading, collision/visual geometry support, and timer-driven execution for the MPC node; fixed critical MPC frame naming and duration conversions; removed deprecated files; improved documentation and dependency handling. Business value: prepared the controller for ROS2 production deployments with configurable, maintainable parameters; improved runtime reliability and modularity; reduced risk and maintenance overhead.
December 2024
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary: Delivered MPC-based Optimal Control for goal pose with ROS integration in the agimus_controller repository, enabling predictive tracking of target poses for the robot arm and tighter integration with the HPP interface. Implemented the Optimal Control Problem formulation, parameters, and cost functions, and integrated it into the main robot model script. Added a new MPC-driven ROS node to execute control commands, manage data saving, subscribe to sensors, and publish actuation commands. Updated build system, launch configurations, and API to support MPC workflows, with accompanying documentation improvements. Initiated ROS migration groundwork for agimus_controller with a main controller node and HPP subscriber, plus launch files and package configurations to establish foundational control infrastructure. Business value: enables faster experimentation with predictive control, improves pose-tracking accuracy and reliability, and creates a scalable ROS-based control backbone for future features.
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary: Delivered MPC-based Optimal Control for goal pose with ROS integration in the agimus_controller repository, enabling predictive tracking of target poses for the robot arm and tighter integration with the HPP interface. Implemented the Optimal Control Problem formulation, parameters, and cost functions, and integrated it into the main robot model script. Added a new MPC-driven ROS node to execute control commands, manage data saving, subscribe to sensors, and publish actuation commands. Updated build system, launch configurations, and API to support MPC workflows, with accompanying documentation improvements. Initiated ROS migration groundwork for agimus_controller with a main controller node and HPP subscriber, plus launch files and package configurations to establish foundational control infrastructure. Business value: enables faster experimentation with predictive control, improves pose-tracking accuracy and reliability, and creates a scalable ROS-based control backbone for future features.
Activity
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Quality Metrics
Correctness84.8%
Maintainability85.2%
Architecture82.4%
Performance72.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
C++CMakeMarkdownPythonShellXMLYAMLyaml
Technical Skills
Abstract Base ClassesBuild System ConfigurationC++C++ (via bindings)Code CleanupCode ManagementCode OrganizationCode RefactoringCollision DetectionComputer VisionConfigurationConfiguration ManagementConfiguration SpaceControl SystemsData Engineering
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