Phd Position F/m Learning-based Control of An
Il y a 6 mois
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
**Contexte et atouts du poste**:
- The work will be carried in English at the Inria Rennes Bretagne Atlantique research center.
- The Ph.D. position is full-time for 3 years (standard duration in France). The position will be paid according to the French salary regulations for PhD students.
- We do high quality and impactful research in robotics, publishing on the major journals and conferences.
- We often collaborate with other top researchers in europe and worldwide.
- You will have access to a well established laboratory including:
- two flying arenas equipped with motion tracking system, several quadrotors, and a few fully-actuated manipulators,
- one robotic manipulation lab equipped with several robotic arms, like the Franka Emika Panda.
- You will be part of an international and friendly team. We organize several events, from after works, to multi-day lab retreat.
- Regular visits and talks by internationally known researchers from top research labs.
**Mission confiée**:
**Short abstract**:
- trol methods, we want to make aerial manipulators able to perform much complex tasks,going beyond the current performance of model-based strategies.**Description**:
- over often performed in known/controlled and structured environments (i.e., in ideal lab conditions). Most achieved tasks belong to the family known as push & slide paradigm, which consists in simply touching a wall at different locations with a single point contact end
- effector while controlling the interaction force [1, 2]. Preliminary results, showing robots opening doors and valves has been presented. Those methods are strongly model based and lack of robustness. Moreover, most works performed such a task in indoor controlled environments where the robot position is measured with accurate motion capture systems (MOCAPs) and the environment is perfectly known [3].
- This PhD thesis aims at pioneering this still mostly unexplored domain, pushing further the boundaries of APhI. In contrast to the current state of the art, our goal is to enhance aerial robotic physical interaction capabilities of highly dynamical aerial manipulators (AMs) by considering manipulations tasks of articulated and dynamic objects. The project will focus on the design of new data and learning-based control algorithms to make aerial robots much more precise, robust and safe while performing physical interaction tasks involving articulated objects or humans, in real environments. In particular we want to compare, combine, and finally enhance model-based methods that will be developed within the ANR project AirHandyBot. As a final demonstrator we want to show an aerial robot equipped with an articulated arm capable to open a door with onboard sensors only in a reliable and safe way.
**Principales activités**:
The work will address the following points:
- **Data-enhanced model**: Previous modeling methods assume a priori knowledge of the geometric and dynamic parameters. However these can be inaccurate or might change in real environments. Another important aspect, often considered negligible, are model uncertainties. To cope with them, we propose to improve the accuracy of the models developed in the previous tasks, exploiting observations and measurements of the system.
- **Robust manipulation planner**: Improve the manipulation planner planner such that it generates motions that are intrinsically robust to model uncertainties. For this we will rely on the concept of deep reinforcement learning methods.
- **Learning-based full-body controller**:Motivated by the impressive results of RL obtained on legged robots and racing drones, we will investigate if and how these methods can outperform model-based controllers.
- **Experimental validation**: All previous tasks will be validated with real experiments. As a final demonstrator we want to show an aerial robot equipped with an articulated arm capable to manipulate articulated objects, e.g., open a door, using onboard sensors only.
**Avantages**:
- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking (90 days per year) and flexible organization of working hours
- Partial payment of insurance costs
**Rémunération**:
Monthly gross salary amounting to 2082 euros for the first and second years and 2190 euros for the third year
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