7 papers
TaPeR: Probabilistic Recovery of Sparse Task Precedence Graphs from a Handful of Demonstrations
Adrian Röfer, Karla Stepanova, Abhinav Valada
Long-horizon manipulation tasks are often only partially ordered. For example, when assembling an electronic device, the battery and circuit board may be installed in either order,…
See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming
Petr Vanc, Jan Kristof Behrens, Václav HlavÃ¡Ä +1
Programming by demonstration (PbD) makes robot programming accessible to non-experts, but scaling it to real-world variability remains a challenge for current teaching frameworks,…
Learning Transferable Motor Skills for Geometry-Aware Robotic Surface Tasks
Miroslav David, Karla Stepanova, Robert Babuska
Robotic surface-interaction tasks, such as spray painting or welding, require both accurate geometric planning and precise motion execution. While modern motion planners generate v…
How Should We Teach Robots? A Comparison of Kinesthetic, Joystick, and Gesture-Based Teaching
Petr Vanc, Jan Kristof Behrens, Václav HlavÃ¡Ä +1
Instructing robots from demonstrations can be done through different teaching modalities, each with different usability and performance trade-offs. This paper compares kinesthetic…
Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming
Oleh Borys, Karla Stepanova
Learning from Demonstration~(LfD) should capture not only how a task is executed, but also its high-level task structure that explains the demonstrated behavior. As robots become m…
ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input
Petr Vanc, Giovanni Franzese, Jan Kristof Behrens +4
Learning from demonstration is a promising approach for teaching robots new skills. However, a central challenge in the execution of acquired skills is the ability to recognize fau…