62 citations · 107 across the 7 of their papers we have counts for
7 papers
Motion Mappings for Continuous Bilateral Teleoperation
Xiao Gao, João Silvério, Emmanuel Pignat +3
Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smo…
Towards Orientation Learning and Adaptation in Cartesian Space
Yanlong Huang, Fares J. Abu-Dakka, João Silvério +1
As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint s…
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
João Silvério, Yanlong Huang, Fares J. Abu-Dakka +2
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extra…
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
João Silvério, Yanlong Huang, Leonel Rozo +2
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space)…
Kernelized Movement Primitives
Yanlong Huang, Leonel Rozo, João Silvério +1
Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human…
Learning Task Priorities from Demonstrations
João Silvério, Sylvain Calinon, Leonel Rozo +1
Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. W…
Generalized Task-Parameterized Skill Learning
Yanlong Huang, João Silvério, Leonel Rozo +1
Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of…