1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
Interactive Human-in-the-loop Coordination of Manipulation Skills Learned from Demonstration
Meng Guo, Mathias Buerger
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial appl…
Geometric Task Networks: Learning Efficient and Explainable Skill Coordination for Object Manipulation
Meng Guo, Mathias Bürger
Complex manipulation tasks can contain various execution branches of primitive skills in sequence or in parallel under different scenarios. Manual specifications of such branching…
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…
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Andras Kupcsik, Markus Spies, Alexander Klein +4
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suit…