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Neural Contractive Dynamical Systems
Stability guarantees are crucial when ensuring a fully autonomous robot does not take undesirable or potentially harmful actions.
Learning Riemannian Stable Dynamical Systems via Diffeomorphisms
This is challenging as the robot state does not evolve on a Euclidean space, and therefore the stability guarantees and vector field encoding need to account for the geometry arising from, for example, the orientation representation.
Reactive Motion Generation on Learned Riemannian Manifolds
We argue that Riemannian manifolds may be learned via human demonstrations in which geodesics are natural motion skills.
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning
Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays.
Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly.
