By Aleksandar Vakanski, Farrokh Janabi-Sharifi
This publication provides programming via demonstration for robotic studying from observations with a spotlight at the trajectory point of activity abstraction
- Discusses equipment for optimization of job replica, resembling reformulation of activity making plans as a restricted optimization problem
- Focuses on regression ways, equivalent to Gaussian mix regression, spline regression, and in the neighborhood weighted regression
- Concentrates at the use of imaginative and prescient sensors for taking pictures motions and activities in the course of job demonstration through a human activity expert
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Extra resources for Robot learning by visual observation
Robot learning from demonstration by constructing skill trees. International Journal of Robotics Research, vol. 3, no. 1, pp. 360–375. , (2014). Learning compliant manipulation through kinesthetic and tactile human–robot interaction. IEEE Transactions on Haptics, vol. 7, no. 3, pp. 367–380. , (2010). Learning actions from observations: primitive-based modeling and grammar. Robotics and Automation Magazine, vol. 17, no. 2, pp. 30–43. , (2002). Mobile robot programming using natural language. Robotics and Autonomous Systems, vol.
Representing the demonstrations at a trajectory level is convenient for specifying the velocities and accelerations at different phases of the demonstrated tasks, as well as for defining the spatial task constraints. This type of task representation also allows encoding of arbitrary gestures and motions, conversely to the symbolic-based representation, where the task representation requires prior knowledge about the elementary components that comprise the demonstrated motions. , statistical models) to reduce the dimensionality of the segmented signals.
In the approach proposed by Chernova and Veloso (2008b) if the robot is not confident about some elements of the demonstrated task, it requests the teacher to perform additional demonstration(s). This way the robot incrementally builds the task model until the level of confidence for the entire task is above a certain threshold value. Still, it is not clear how to automate the process of selection of training examples without relying on the generalization abilities of the teacher. Aleotti and Caselli (2006) proposed a distance metric for clustering the demonstrations into similar patterns, so that the clusters of trajectories can be treated as different skills.
Robot learning by visual observation by Aleksandar Vakanski, Farrokh Janabi-Sharifi