Invited speakers

Technical University of Munich

German Aerospace Center

The geometry of nonlinear oscillation modes of robotic systems and their application to locomotion des and manipulation

Max Planck Institute for Intelligent systems

Riemannian manifolds learned from data

Stanford University

A Geometric Take on Probabilistic Synchronization

Stanford University

Bridging Topology and Geometry in Deformable Object Manipulation

St. Petersburg Department of Steklov Mathematical Institute

Imperial College London

Gaussian Processes on Riemannian Manifolds for Robotics

Imperial College London

Twitter

Geometric Deep Learning: The Erlangen Programme of ML

Institute of Science and Technology (IST)

Distortion, on the Average and in Expectation

University of Pennsylvania

The Role of Topology in Robotics

University of Oulu

Goldilocks and the Robot Brains

University of Washington

Safe and Efficient Robot Learning Using Riemannian Motion Policies

University of Toronto and University of Zagreb

Of Ellipsoids and Distances: Reconsidering Robot Kinematics

Technical University of Berlin

Multilevel Motion Planning

Arizona State University

Topological methods in modeling human activity

KTH Royal Institute of Technology

Topological tools in complex manipulation