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Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control
In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans.
Imitation and Supervised Learning of Compliance for Robotic Assembly
This can be used to learn a suitable representation of the skill that can be generalized to novel positions of one of the parts involved in the assembly, for example the hole in a peg-in-hole (PiH) insertion task.
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.
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