IPAB Workshop-06/02/2020

 

 

Title: On the need for inductive biases in robot learning

 

Abstract: Advances in deep learning and deep reinforcement learning have resulted in an explosion of researchers naively throwing learning algorithms at robots. Time and time again, these approaches fail in real world settings, because robotics is not i.i.d, and is subject to numerous constraints around safety, alongside physical and sensory limitations. Using a learning from demonstration setting, this talk will discuss the need for greater structure and prior knowledge in deep learning models for robot learning and control. In particular, I will focus on how representation learning can be improved using knowledge of desired control strategies, process dynamics and symbolic constructs, or even statistical information about the generative process of demonstration sequences as a supervisory signal.

Feb 06 2020 -

IPAB Workshop-06/02/2020

Michael Burke

IF, G.03