Doctorant F/H PhD Position: Beyond reinforcement learning for social adaptation

Doctorant F/H PhD Position: Beyond reinforcement learning for social adaptation The offer description be low is in French Level of qualifications required : Graduate degree or equivalent Fonction : PhD Position Context Context and funding:This position is funded by the PEPR AS3project.Within this framework, the HUCEBOT team is developing multimodal strategies for online control and adaptation of dynamic legged robot platforms.This PhD project explores quality diversity optimization as an alternative to traditional reinforcement learning for robot control. While reinforcement learning typically converges to a single solution for a specific task, quality diversity methods learn repertoires of policies that can solve different tasks or approach the same task in multiple ways. The research will investigate structured approaches to policy search, combining behavioral exploration, diversity-driven optimization, and meta-learning for rapid adaptation. A particular focus will be on integrating multimodal human feedback to enable intuitive adaptation in collaborative scenarios involving physical interaction, such as assistive manipulation or wearable exoskeletons. The goal is to develop more flexible and adaptable robot behaviors suited for real-world human-robot collaboration. 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. Assignment This PhD project explores quality diversity optimization as an alternative to traditional reinforcement learning for robot control. While reinforcement learning typically converges to a single solution for a specific task, quality diversity methods learn repertoires of policies that can solve different tasks or approach the same task in multiple ways. The research will investigate structured approaches to policy search, combining behavioral exploration, diversity-driven optimization, and meta-learning for rapid adaptation. A particular focus will be on integrating multimodal human feedback to enable intuitive adaptation in collaborative scenarios involving physical interaction, such as assistive manipulation or wearable exoskeletons. The goal is to develop more flexible and adaptable robot behaviors suited for real-world human-robot collaboration. The candidate will collaborate with Guillaume Bellegarda (researcher), Enrico Mingo Hoffman (researcher), Jean-Baptiste Mouret (researcher), Serena Ivaldi (researcher), and Mohamed Chetouani (Professor, Sorbonne Université). Main activities Review state-of-the-art in quality diversity optimization and meta-learning for robot control. Develop learning-based frameworks for acquiring diverse policy repertoires. Investigate online adaptation and policy selection strategies for dynamic task variations. Integrate multimodal human feedback for adaptive human-robot collaboration. Deploy and validate controllers in hardware experiments on collaborative robotic platforms. Analyze results, write papers and dissertation. Skills Technical skills: Background in robotics, control, machine learning. Excellent skills and/or experience with simulation frameworks (i.e. Isaac, MuJoCo), reinforcement learning, and motion planning Excellent skills in Python, C++, ROS Interest and preferably experience in manipulation andlegged robots Excellent communication skills at work, and ability to report progress Not afraid of challenging projects. Rigour 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 Benefits package Theme/Domain :Robotics and Smart environmentsScientific computing(BAP E) Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Defence Security :This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy :As part of its diversity policy, all Inria positions are accessible to people with disabilities. Excellent communication in English, French is a plus. Ideal profile: Master Degree studies in Robotics, Control, Engineering, Bio-engineering, or relevant fields. *** IMPORTANT ***We will only consider applications with the following attachments (add them in the PDF when completing the application): 1) Transcripts of your University classes (Bachelor, Master, Engineering School...) 2) Your CV should mention all the places where you studied, starting from High School, so please list High School, University, Bachelor/Master, College, Engineering School, Classe Preparatoires, etc. Please mention city and country. About Inria Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. #J-18808-Ljbffr