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Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
We unify previous adaptation techniques, for example, various types of obstacle avoidance, via-points, mutual avoidance, in one single framework and combine them to solve complex robotic problems.
Robotics
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity.
Active Learning based on Data Uncertainty and Model Sensitivity
It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i. e., transitions that lead to abrupt changes in the movement of the robot.
Probabilistic Movement Primitives
In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution.
