
Joseph Mirabel developed advanced model predictive control and trajectory planning features for the agimus-project/agimus_controller repository, focusing on robotics applications using Python, C++, and ROS. Over six months, he engineered modular MPC warm-start strategies, robust collision detection, and multithreaded solver integration, improving both performance and reliability. His work included YAML-configurable optimal control problem definitions, dynamic trajectory publishing, and visualization tools for debugging in RViz. By refactoring core components and enhancing test coverage, Joseph ensured maintainable, extensible code. His technical approach emphasized asynchronous programming, code simplification, and robust parameter handling, resulting in a flexible, production-ready control stack for robotic systems.

June 2025 monthly summary for agimus_controller focusing on key deliverables, reliability improvements, and scaffolding for future enhancements. Highlights include feature delivery for MPC debugging visualization and cost plotting, reliability improvements in MPC startup and collision detection, extended collision-residual modeling with ActivationModelExp, and broad code quality and UX enhancements. These efforts drive faster debugging, safer operations, and more robust MPC integration in production workflows.
June 2025 monthly summary for agimus_controller focusing on key deliverables, reliability improvements, and scaffolding for future enhancements. Highlights include feature delivery for MPC debugging visualization and cost plotting, reliability improvements in MPC startup and collision detection, extended collision-residual modeling with ActivationModelExp, and broad code quality and UX enhancements. These efforts drive faster debugging, safer operations, and more robust MPC integration in production workflows.
May 2025 monthly summary for agimus-controller highlighting business value from key feature deliveries, major bug fixes, and overall impact. Focused on performance, reliability, and maintainability of the ROS-based MPC stack.
May 2025 monthly summary for agimus-controller highlighting business value from key feature deliveries, major bug fixes, and overall impact. Focused on performance, reliability, and maintainability of the ROS-based MPC stack.
April 2025 delivered modular trajectory publishing and robust visual servoing for agimus_controller, enhancing multi-joint control flexibility, runtime robustness, and ROS integration. The work enables easier extension of trajectory strategies, improves error handling and dynamic pose updates, and speeds up feature delivery for robotics applications.
April 2025 delivered modular trajectory publishing and robust visual servoing for agimus_controller, enhancing multi-joint control flexibility, runtime robustness, and ROS integration. The work enables easier extension of trajectory strategies, improves error handling and dynamic pose updates, and speeds up feature delivery for robotics applications.
2025-03 Highlights: Delivered a configurable, ROS-integrated OCP framework with YAML-based definitions, enabling reusable planning components; Refined MPC/Agimus initialization and startup with new init callback and granular buffer checks, improving startup stability; Launched MPC debugging visualization (mpc_debugger_node) for RViz, accelerating validation against the robot's kinematic model; Enhanced solver robustness and performance with new iteration/timeout controls and refined CSQP handling, reducing risk of stalls during real-time planning; Expanded testing and documentation for OCP/CrocoGeneric and MPC data structures, increasing test coverage and maintainability; Fixed trajectory length safeguard in OCPCrocoGoalReaching to preserve compatibility with issue #198, reducing runtime errors in edge cases.
2025-03 Highlights: Delivered a configurable, ROS-integrated OCP framework with YAML-based definitions, enabling reusable planning components; Refined MPC/Agimus initialization and startup with new init callback and granular buffer checks, improving startup stability; Launched MPC debugging visualization (mpc_debugger_node) for RViz, accelerating validation against the robot's kinematic model; Enhanced solver robustness and performance with new iteration/timeout controls and refined CSQP handling, reducing risk of stalls during real-time planning; Expanded testing and documentation for OCP/CrocoGeneric and MPC data structures, increasing test coverage and maintainability; Fixed trajectory length safeguard in OCPCrocoGoalReaching to preserve compatibility with issue #198, reducing runtime errors in edge cases.
February 2025 — agimus_controller: Delivered measurable improvements across MPC control validation, test reliability, and data access. Key work included stabilizing the MPC Unicycle controller tests and trajectory handling; hardening the test suite; strengthening warm-start generation through explicit exposure of RobotModels and OCPParamsBaseCroco in setup with added tests; exposing RobotModels Armature API to simplify data access; improving horizon size handling and trajectory timing for robust planning; and enhancing OCPCrocoGeneric with a YAML-configurable OCP class, plus improvements to TrajectoryPoint state representations and delay compensation. These changes collectively increase control reliability, shorten validation cycles, and enable more flexible experimentation and deployment.
February 2025 — agimus_controller: Delivered measurable improvements across MPC control validation, test reliability, and data access. Key work included stabilizing the MPC Unicycle controller tests and trajectory handling; hardening the test suite; strengthening warm-start generation through explicit exposure of RobotModels and OCPParamsBaseCroco in setup with added tests; exposing RobotModels Armature API to simplify data access; improving horizon size handling and trajectory timing for robust planning; and enhancing OCPCrocoGeneric with a YAML-configurable OCP class, plus improvements to TrajectoryPoint state representations and delay compensation. These changes collectively increase control reliability, shorten validation cycles, and enable more flexible experimentation and deployment.
January 2025: End-to-end MPC warm-start improvements for the crocoddyl unicycle model in agimus_controller. Implemented WarmStartShiftPreviousSolution, enhanced unicycle warm-start handling, improved robustness, and removed obsolete WarmStartReference. Added unit tests and plotting utilities to support MPC integration. Work aligns with latest crocoddyl updates and passed initial reviews.
January 2025: End-to-end MPC warm-start improvements for the crocoddyl unicycle model in agimus_controller. Implemented WarmStartShiftPreviousSolution, enhanced unicycle warm-start handling, improved robustness, and removed obsolete WarmStartReference. Added unit tests and plotting utilities to support MPC integration. Work aligns with latest crocoddyl updates and passed initial reviews.
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