Position description
Category
Mathematics, information, scientific, software
Contract
Internship
Job title
Stage From Gen-AI grasp synthesis to bimanual robotic manipulation-Saclay-H/F
Subject
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.
Contract duration (months)
6 mois
Job description
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:
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
Methods / Means
Python - PyTorch
Applicant Profile
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.
Position location
Site
Saclay
Job location
France, Ile-de-France, Essonne (91)
Location
Saclay
Candidate criteria
Languages
Prepared diploma
Bac+5 - Diplôme École d'ingénieurs
PhD opportunity
Oui
Requester
Position start date
01/02/2027
Category
Mathematics, information, scientific, software
Contract
Internship
Job title
Stage From Gen-AI grasp synthesis to bimanual robotic manipulation-Saclay-H/F
Subject
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.
Contract duration (months)
6 mois
Job description
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:
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
Methods / Means
Python - PyTorch
Applicant Profile
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.
Position location
Site
Saclay
Job location
France, Ile-de-France, Essonne (91)
Location
Saclay
Candidate criteria
Languages
- English (Fluent)
- French (Fluent)
Prepared diploma
Bac+5 - Diplôme École d'ingénieurs
PhD opportunity
Oui
Requester
Position start date
01/02/2027
