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Safe Simulation to Real World Transfer
The project aims to develop techniques based on machine learning to have maximal knowledge transfer between simulated and real world on a navigation task.
Recent techniques based on machine learning enabled robotics system to perform many difficult tasks, such as manipulation or navigation.
Those techniques are usually very data-intensive, and require simulators to generate enough training data.
In this project, we will develop techniques to formalize the notion of simulation to reality transfer in a robotics setting.
In particular, we are interested in finding performance guarantees to bound the performance drop usually encountered when deploying a policy trained in simulation on a physical platform.
Recent techniques based on machine learning enabled robotics system to perform many difficult tasks, such as manipulation or navigation. Those techniques are usually very data-intensive, and require simulators to generate enough training data. In this project, we will develop techniques to formalize the notion of simulation to reality transfer in a robotics setting. In particular, we are interested in finding performance guarantees to bound the performance drop usually encountered when deploying a policy trained in simulation on a physical platform.
The project aims to develop techniques based on machine learning to have maximal knowledge transfer between simulated and real world on a general robotic task.
The project aims to develop techniques based on machine learning to have maximal knowledge transfer between simulated and real world on a general robotic task.