I am the scientific coordinator of the JCJC Project ANR-24-CE33-0753-01 MeRLin: Multi-limbed Robots empowered by whole-body Loco-manipulation.
Summary
The MeRLin project addresses the limitations of current robotics in performing hazardous and strenuous tasks, which are still predominantly carried out by human workers in industries like construction and heavy manufacturing. Such tasks often expose workers to significant safety and health risks, including long-term musculoskeletal disorders. Motivated by the need to create safer working environments and enhance productivity, the project focuses on developing advanced robotics that can operate in unstructured and dynamic environments.
The project aims to develop a robot-agnostic framework that enables multi-limbed robots to perform complex locomotion and manipulation tasks by coordinating their entire bodies. It addresses two primary scientific challenges:
- Whole-body planning: Developing strategies for planning long-term, contact-rich motions that consider the robot’s physical interactions with the environment and manipulated objects.
- Whole-body control: Creating robust, fast-reacting controllers that adapt to changing environments and execute planned tasks effectively.
To achieve these goals, MeRLin proposes a novel integration of model-based optimization techniques (e.g., trajectory optimization and Model Predictive Control) with deep reinforcement learning (DRL):
- Model-based techniques provide guarantees of stability and constraint satisfaction, enabling long-term planning of efficient and feasible robot movements.
- Reinforcement learning ensures robustness and adaptability in dynamic and unpredictable environments, leveraging sensor data like RGB-D cameras and proprioceptive feedback.
A synergy of these methods creates a learning-augmented planning and control framework, which also accelerates learning processes and avoids poor local optima.
MeRLin stands as a timely initiative to meet the growing industry interest in humanoid and multi-limbed robots, as evidenced by significant investments in bipedal robotics from companies like Tesla and Agility Robotics.
Loria scientific team
- Enrico Mingo Hoffman (PI)
- Ioannis Tsikelis (Ph.D. candidate, through INRIA Ph.D. grant)
- Ioannis Loizou (Ms. Student from Ecole Centrale de Nantes, February to July 2025)
- Evangelos Tsiatsianas (Ms. Student from University of Patras sponsored by the ORION Programme, September 2025 to February 2026)
- Fabio Amadio (SRP Researcher)
- Jean-Baptiste Mouret (Senior Research Scientist)
- Serena Ivaldi (Senior Research Scientist)
Activities
- Visiting period at Sapienza University of Rome
- Collaboration within the ANR Project MeRLin between the INRIA and IIT
- OptRob-25: Optimization for Robotics Summer School, July 14-18, 2025, at the University of Patras, Patras, Greece.
- Invited Lecturer at UCL, London, UK
Results
- E. Mingo Hoffman, A. Laurenzi, and N. G. Tsagarakis, “The Open Stack of Tasks Library: OpenSoT: A Software Dedicated to Hierarchical Whole-Body Control of Robots Subject to Constraints,” in IEEE Robotics & Automation Magazine, November 2024, [HAL][DOI]
- E. Mingo Hoffman, D. Costanzi, G. Fadini, N. Miguel, A. Del Prete, and L. Marchionni, “Addressing Reachability and Discrete Component Selection in Robotic Manipulator Design through Kineto-Static Bi-Level Optimization,” in IEEE Robotics and Automation Letters, January 2025, [HAL][DOI]
- L. Rossini, F. Ruscelli, Q. Rouxel, N. Tsagarakis, E. Mingo Hoffman, “Experimental Validation of Open-Loop Walking based on Whole-Body Model Predictive Control“, in HAL Open Science, April 2025, [HAL]
- I. Loizou, “Humanoid Bipedal Locomotion through Linear Model Predictive Control”, Master Thesis Report, August 2025, [HAL]
- N. Scianca and E. mingo Hoffman, “CPC: Cascaded Predictive Control”, in IEEE-RAS International Conference on Humanoid Robotics (Humanoids), October 2025, [HAL][DOI]
- I. Tsikelis, E. Tsiatsianas, C. Kiourt, S. Ivaldi, K. Chatzilygeroudis, and E. Mingo Hoffman, “AHMP: Agile Humanoid Motion Planning with Contact Sequence Discovery”, in IEEE-RAS International Conference on Humanoid Robotics (Humanoids), October 2025, [HAL][DOI]
- F. Amadio, H. Li, L. Uttini, D. Kanoulas, S. Ivaldi, V. Modugno, and E. Mingo Hoffman, “Learning to Walk with Hybrid Serial-Parallel Linkages: A Case Study on the Kangaroo Robot”, in International Conference on Robot Intelligence Technology and Applications (RiTA), December 2025, [HAL]
- K. Chatzilygeroudis, S. Kumar, B. Plancher, Z. Manchester, E. Mingo Hoffman, and P. Wensing, “The 2025 Summer School on Optimization for Robotics [Education]”, in IEEE Robotics & Automation Magazine, December 2025[HAL][DOI]
