3 citations · 6 across the 4 of their papers we have counts for
7 papers · 1 filter
Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation
Michael Przystupa, Gauthier Gidel, Matthew E. Taylor +3
As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-free…
Unsupervised Learning of Effective Actions in Robotics
Marko Zaric, Jakob Hollenstein, Justus Piater +1
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-the-art action representations in ro…
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
Sayantan Auddy, Sebastian Bergner, Justus Piater
In continual learning from demonstration (CLfD), a robot learns a sequence of real-world motion skills continually from human demonstrations. Recently, hypernetworks have been succ…
Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills
Hector Perez-Villeda, Justus Piater, Matteo Saveriano
In Programming by Demonstration, the robot learns novel skills from human demonstrations. After learning, the robot should be able not only to reproduce the skill, but also to gene…
Active and Transfer Learning of Grasps by Kernel Adaptive MCMC
Philipp Zech, Hanchen Xiong, Justus Piater
Human ability of both versatile grasping of given objects and grasping of novel (as of yet unseen) objects is truly remarkable. This probably arises from the experience infants gat…
Active and Transfer Learning of Grasps by Sampling from Demonstration
Philipp Zech, Justus Piater
We guess humans start acquiring grasping skills as early as at the infant stage by virtue of two key processes. First, infants attempt to learn grasps for known objects by imitatin…