DESCRIPTION
This tutorial provides an in-depth exploration of robot learning, focusing on how machine learning techniques enable robots to learn and adapt through interaction with their environments. It covers core methods in imitation learning (IL), such as behavior cloning and addressing distribution shifts, and extends to advanced topics like generative models and privileged teachers. Additionally, the tutorial delves into reinforcement learning (RL) strategies, highlighting approaches such as Deep Q-learning, Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC). The ultimate goal discussed is what are the techniques that we currently investigate in the field of robot learning for equipping robots with general-purpose embodied intelligence, empowering them to autonomously handle dynamic, real-world tasks through continuous learning.
DETAILS (to be updated)
Course type: Tutorial
Institution of lecturer: Computer Science Department, Technische Universität Darmstadt
LECTURER
Prof. Georgia Chalvatzaki