From Robot Learning To Robot Understanding: Leveraging Causal Graphical Models For Robotics


Causal graphical models have been proposed as a way to efficiently and explicitly reason about novel situations and the likely outcomes of decisions. A key challenge facing widespread implementation of these models in robots is using prior knowledge to hypothesize good candidate causal structures when the relevant environmental features are not known in advance. The tight link between causal reasoning and the ability to intervene in the world suggests that robotics has much to contribute to this challenge and would reap significant benefits from progress.

Publication type
5th Annual Conference on Robot Learning, Blue Sky Submission Track


This was the first “blue-sky” track at CoRL, and I feel very fortunate to have been given the opportunity to present there. You can watch the panel session I was part of here (I’m the first of 4 speakers, and then there’s a panel after each of our talks).