Thesis of Pierre Corbani
Subject:
Sparse Mixture of Experts-Enhanced Robot Learning for Foundation Models in Robotics
Start date: 01/12/2025
End date (estimated): 01/12/2028
Advisor: Liming Chen
Summary:
This task aims to investigate Sparse MoE-enhanced Foundation Models for Robotics (MoE-FMR) that enable efficient, scalable, and transferable robotic skill learning.