Stage From Gen-Ai Grasp Synthesis To Bimanual Robotic Manipulation-Saclay H/F CEA
- Saclay - 91
- Stage
- 6 mois
- Bac +5
- Industrie high-tech • Telecom
À noter sur ce job
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Les missions du poste
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.
Context
While 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:
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.
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Le profil recherché
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.
Les avantages
- Télétravail jusqu’à 3 jours par semaine
- 52 jours de congés/RTT
- Possibilité d’aménagement du temps de travail
- Formation personnalisée
- Restauration d’entreprise
- Offre de transport interne et prise en charge Navigo and co,
- Mutuelle d’entreprise avantageuse
- CE (aides vacances, loisirs, frais de garde, scolarité des enfants etc
Les étapes de recrutement
Les étapes de recrutement peuvent varier selon l'offre à laquelle vous postulez.
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Dépôt de CV via notre site carrière
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Préqualification téléphonique
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Entretiens et évaluation avec manager et RH
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Négociation salariale et contrat de travail
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Embauche et intégration
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Publiée le 05/10/2026 - Réf : 2026-41845