The field of robotics is an end user and a motivator for methods developed in the closely related fields of control, computer vision, machine learning, and artificial intelligence. Such developments often occur in different scientific communities using different methodologies, languages, and underlying working assump- tions. This workshop seeks to advance the core themes underlying these disciplines and their mathematical underpinnings. Topics will include stochastic analysis and control, the geometry of spatial information and sensory signal processing, and geometric and algebraic structures which arise naturally in robotics, percep- tion and learning. Biology provides existence proofs for efficient algorithms and systems, still the paragon in many tasks that require interacting with highly uncertain, dynamic, and complex environments. Learning, perception and control are fundamental modalities necessary for autonomous systems to operate in dynamic, uncertain and remote environments. Autonomous systems should be able to robustly walk, navigate, efficiently explore, quickly learn new motor skills and generalize these skills to unseen conditions.
This workshop will bring together scientists from different areas of sciences and engineering to brainstorm on two questions related to the representation of sensory information and data, and generalization of decision and control mechanisms in robotics and autonomous systems. The aforementioned topics will be investigated at the intersection of planning and control, information theory, machine learning, neuroscience and cognitive sciences, and perception. The emphasis of the workshop will be on the mathematical interdependencies and interconnections of these areas based on concepts drawn from differential geometric and topology. The workshop will also determine future research directions and identify open questions across the disciplines of control theory, machine learning, perception and cognitive sciences. These future research directions can bring autonomy into a new level and create new areas of investigation at the frontier of robust intelligence and autonomy.
Dr. Dieter Fox (UW), Dr. Byron Boots (Gatech), Dr. Pedro Domingos (University of Washington), Dr. Roberto Tron (BU) Dr. Frank Dellaert (Georgia Institute of Technology), Dr. John Tsotsos (York University), Dr. Volkan Isler (UofM), Dr. Yiannis Aloimonos (UM) Dr. Sergey Levine (UW), Dr. Tyrone Duncan (University of Kansas), Dr. Emannuel Todorov (UW), Dr. Pedro Ortega (UPENN), Dr. Bert Kappen (Radhoud University Nijmegen), Dr. Sertac Karaman (MIT), Dr. Marin Kobilarov (JHU), Dr. Taeyoung Lee (George Washington University) Dr. Melvin Leok (UCSD), Dr. Andrea Censi (MIT), Dr. P.S.Krishanprasad (UM), Dr.Panos Tsiotras (Gatech) Dr. Jonathan How (MIT), Dr. Russ Tedrake (MIT), Dr. Joel Burdick (Caltech), Nikolay Atanasov (UPENN), Dr. Marco Pavone (Stanford), Dr. Adrian Haith (JHU), Dr.Veronica Santos (UCLA), Dr. Gary McGrath (Qualcomm), Dr. Evangelos Theodorou (Gatech), Dr. Stefano Soatto (UCLA)