Stage From Gen-AI grasp synthesis to bimanual robotic manipulation-Saclay-

Mathematics, information, scientific, software Stage From Gen-AI grasp synthesis to bimanual robotic manipulation-Saclay-H/F From Gen-AI grasp synthesis to bimanual robotic manipulation H/F This internship bridges the gap between state-of-the-art generative grasp planners and real-world physical execution. The core objective is to develop a robust robotic skill layer — integrating 6D object pose estimation with dynamic hand control — that enables a bi-manual dexterous setup to reliably solve complex assembly tasks. As an intern at the CEA, you will have the opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work performed may potentially lead to a scientific publication.ContextWhile generative AI has significantly advanced grasp synthesis in simulation, deploying these models onto physical hardware remains a critical bottleneck. End-to-end learning methods often require prohibitively large datasets of real-world demonstrations, making modular, perception-driven pipelines a highly promising alternative for complex manipulation. This internship bridges the gap between state-of-the-art generative grasp planners and real-world physical execution. The core objective is to develop a robust robotic skill layer — integrating 6D object pose estimation with dynamic hand control — that enables a bi-manual dexterous setup to reliably solve complex assembly tasks. The following works conducted at the lab will serve as a basis for the internship:https://cea-list.github.io/cotograspweb/https://cea-list.github.io/BOP-Distrib/ What do we expect fron you?The internship is research oriented. The chosen candidate will work closely with a PhD candidate. The missions will be the following: • Sim-to-Real Grasp Execution: Deploy and evaluate our existing generative AI grasp planners [1, 2] on physical dexterous hand hardware.• Dynamic Object Perception: Integrate and adapt 6D pose estimation models (e.g., [3]) to accurately localize and track objects of interest.• Robust Grasp Control: Design and implement a dedicated hand controller capable of executing dynamic and reliable grasps based on the generated plans.• Bi-Manual System Integration: Scale and deploy the complete  perception-to-control pipeline onto a dual-arm setup to autonomously solve complex assembly tasks.This internship is designed as a first step before a PhD. Bibliography [1] J. Mérand, B. Meden, L. Chen, and M. Grossard, “GOAG: Generative and object-agnostic grasp planner for dexterous robotic manipulation,” in International Conference on Intelligent Robots and Systems, 2026. [2] J. Mérand, B. Meden, L. Chen, and M. Grossard, “CoToGrasp: Contact-topology-conditioned dexterous grasp synthesis via canonical workspace learning,” in European Conference on Computer Vision, 2026. [3] A. Brazi, B. Meden, F. M. de Chamisso, S. Bourgeois, and V. Lepetit, “Corr2distrib: Making ambiguous correspondences an ally to predict reliable 6d pose distributions,” IEEE Robotics and Automation Letters, 2025. #Cea List Profile :• Currently enrolled in your final year of a Master's program (M2) or an Engineering School (including gap year students). • Practical experience in robotics, specifically with ROS2 knowledge.• Strong skills in computer vision and machine learning, particularly with deep learning, perception models, and generative AI.• High proficiency in Python alongside experience using a deep learning framework, with a strong preference for PyTorch.