The recent advancements in autonomy algorithms and computational hardware have enabled robots to break out of contrived laboratory settings and be deployed in the real world. There is a growing demand for modern civilian and military systems to not only exhibit complex and intelligent behavior in response to external stimuli, but to augment capabilities through machine learning techniques. In order for complex robotic systems to demonstrate intelligent and consistent performance across diverse scenarios that they encounter, it's imperative that a number of distinct, yet interrelated modules (i.e. estimation, decision making, learning, control) are tightly integrated. Today, these individual topics are being widely researched upon in isolation. Hence, this workshop aims to bring together researchers from different communities and discuss problems that occur at the intersection of their fields, present recent advances and contemplate on future directions of research.
We had a fantastic workshop with an excellent set of talks that spanned diverse fields, thought provoking panel discussions, and invigorating exchanges between folks from the controls community and the learning community! The discussions unearthed interesting technical problems that lie at the intersection of various disciplines as well as philosophical problems that require further introspection. While its indeed very hard to summarize the plethora of topics that came up, we highlight a few samples.
The workshop was set to motion by a couple bold claims from the learning community:
3 of the topics from the first panel discussion:
3 of the topics from the second panel discussion: