32 citations · 62 across the 22 of their papers we have counts for
5 papers · 1 filter
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky +3
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics. Many of these pr…
Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Leonel Rozo, Vedant Dave
Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabil…
Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
Akshay Dhonthi, Philipp Schillinger, Leonel Rozo +1
Signal Temporal Logic (STL) is an efficient technique for describing temporal constraints. It can play a significant role in robotic manipulation, for example, to optimize the robo…
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
An T. Le, Meng Guo, Niels van Duijkeren +4
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes an…
Learning Riemannian Manifolds for Geodesic Motion Skills
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis +2
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. This demands learnin…