Research Internship in Learning Dynamic Force-Based Locomotion Skills for Legged Robots

Context and funding:
This position is funded by the PEPR O2R AS3 project.
Within this framework, the HUCEBOT team is developing multimodal strategies for online control and adaptation of dynamic legged robot platforms. This project investigates learning dynamic force-based locomotion skills in legged robots. Current locomotion frameworks often emphasize position-based control rather than the underlying force interactions that govern stability and agility. By focusing on learning time-varying force profiles, the proposed work aims to achieve efficient, adaptive, and dynamically balanced locomotion behaviors, particularly for jumping and other impulsive tasks.

About the team:
The candidate will join the Human Centered Robotics team (HUCEBOT) in the Inria Center of the University of Lorraine in Nancy, France.
The team HUCEBOT develops control, learning, and interaction skills of human-centered robots, such as humanoid, mobile manipulators and exoskeletons. The team develops learning and control algorithms for teleoperated / supervised / autonomous robots, involved in complex manipulation tasks in man-made environments. It also develops AI-based control for wearable exoskeletons designed to assist humans at work, drones and quadrupeds to explore complex environments. The team has excellent robotics facilities, including several humanoid robots (Talos, iCub, G1), manipulators, drones, passive and active exoskeletons, wearable sensors, force plates etc. Its laboratory has a 3D printing facility and a mechatronic workshop for prototyping and maintenance, and a motion capture room with Qualisys and Xsens sensors.
The team consists of many research scientists, postdocs, PhD and has the support of 1 software and 1 mechatronics engineer. The team is international - English and French speaking. French is not required, although free French classes are available in the institute for non-French speakers.

About the laboratory and Nancy:
The Inria Center of the University of Lorraine, is co-located with the Loria laboratory, in the Science and Technology Campus of the University of Lorraine (Nancy, France), next to the Botanical Gardens, at 20 minutes by public transportation or bike from the Nancy train station and City Center. Several student residences and facilities are at walking distance. Nancy is a University town, with a high quality of life and a vibrant student, Erasmus and expat community. Life is Nancy is very affordable compared to Paris, it is easy to find a student residence or apartment. Team members can also access to SUAPS, the University’s sports facilities.

About Nancy in France:
Nancy is the capital of the Grand Est region in France. It is well connected by train to Paris (90 min), Strasbourg (90 min), Luxembourg and Germany. There are direct trains from Nancy to the Paris airport CDG and the Luxembourg airport LUX.
The region around Nancy is ideal for outdoor activities: there are many country trails, long bike trails, forests, mountains, lakes, ski in winter too.

This project investigates learning dynamic force-based locomotion skills in legged robots. Current locomotion frameworks often emphasize position-based control rather than the underlying force interactions that govern stability and agility. By focusing on learning time-varying force profiles, the proposed work aims to achieve efficient, adaptive, and dynamically balanced locomotion behaviors, particularly for jumping and other impulsive tasks.

The internship is for a 4 to 6 months period and must terminate before mid-July.

The candidate will collaborate with Guillaume Bellegarda (researcher) and Serena Ivaldi (researcher). 

• Review state-of-the-art in force and impedance-based control for legged robots. 
• Develop learning-based frameworks for discovering optimal force profiles in legged robots (simulation).
• Generalize learned behaviors to different morphologies and terrains (simulation).
• Deploy controllers in hardware experiments on the Unitree G1 (and possibly other platforms). 
• Analyze results and write report. 

• Technical skills:
• Background in robotics, control, machine learning. 
• Excellent skills and/or experience with simulation frameworks (i.e. Isaac, MuJoCo), reinforcement learning, and force/impedance control
• Excellent skills in Python, C++, ROS 
• Interest and preferably experience in force control and legged robots
• Soft skills:
• Excellent communication skills at work, and ability to report progress
• Not afraid of challenging projects
• Rigor and intellectual honesty
• Curiosity and desire to learn
• Practical mindset and ability to develop robust and reliable solutions
• Autonomy and organizational skills
• Love working in a multi-cultural environment
• Team player

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

€4.35/hour