About the series
Bio: Odest Chadwicke Jenkins, Ph.D., is a Professor of Computer Science and Engineering and Associate Director of the Robotics Institute at the University of Michigan. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from demonstration. His research often intersects topics in computer vision, machine learning, and computer animation. Prof. Jenkins has been recognized as a Sloan Research Fellow and is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). His work has also been supported by Young Investigator awards from the Office of Naval Research (ONR), the Air Force Office of Scientific Research (AFOSR), and the National Science Foundation (NSF). Prof. Jenkins is currently serving as Editor-in-Chief for the ACM Transactions on Human-Robot Interaction. He is a Fellow of the American Association for the Advancement of Science and Association for the Advancement of Artificial Intelligence, and a Senior Member of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers. He is an alumnus of the Defense Science Study Group (2018-19).
Talk Abstract: The visions of interconnected heterogeneous autonomous robots in widespread use are a coming reality that will reshape our world. Similar to "app stores" for modern computing, people at varying levels of technical background will contribute to "robot app stores" as designers and developers. However, current paradigms to program robots beyond simple cases remain inaccessible to all but the most sophisticated of developers and researchers. In order for people to fluently program autonomous robots, a robot must be able to interpret user instructions that accord with that user’s model of the world. The challenge is that many aspects of such a model are difficult or impossible for the robot to sense directly. We posit a critical missing component is the grounding of semantic symbols in a manner that addresses both uncertainty in low-level robot perception and intentionality in high-level reasoning. Such a grounding will enable robots to fluidly work with human collaborators to perform tasks that require extended goal-directed autonomy.
I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by the demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in the workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from a demonstration at scale through messaging protocols suited to web/cloud robotics.
However, there is a larger issue facing our intellectual enterprise: will the technical advancements of robotics and AI benefit and uplift everybody in our society? We are poised to make massive investments to plant the seed corn for the next generation of artificial intelligence and robotics innovations that will undoubtedly continue to shape the future. For society to get the most from this investment, however, we must rethink our approach to scientific peer and merit review to better incentivize good stewardship in our innovation ecosystem. I make the argument that current review processes incentivize behaviors for short-term intellectual speculation - where good stewardship for investing in the long-term health of robotics and AI is often relegated to a null space in deference to short-term opportunism for ratifying existing ideas and people in science. The disparate impacts we see for the underrepresentation of minorities in robotics and AI should be seen as an indicator of the behaviors incentivized in the current innovation ecosystem that also lead to hyper competition in CS degree programs, misconduct in the treatment of colleagues and students, collusion in the review of scientific publications, and stagnation into intellectual local minima. Living up to the spirit and statues arising from the Civil Rights Movement in our endeavors for science offers a viable path for stewardship that will enable AI and Robotics to make the world a better place in actuality.
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