ARE is a non-linear dimensionality reduction technique for embedding observation trajectories, which captures state dynamics that traditional methods do not. The core of ARE is a semidefinite optimization with constraints requiring actions to be distance-preserving in the resulting embedding. Unfortunately, these constraints are quadratic in number and non-local (making recent scaling tricks inapplicable). Consequently, the original formulation was limited to relatively small datasets. This paper describes two techniques to mitigate these issues. We first introduce an action-guided variant of Isomap. Although it alone does not produce actionrespecting manifolds, it can be used to seed conjugate gradient to implicitly solve the primal variable formulation of the ARE optimization. The optimization is not convex, but the Action-Guided Isomap provides an excellent seed often very close to the global minimum. The resulting Scalable ARE procedure gives similar results to original ARE, but can be applied to datasets an order of magnitude larger.
We present a new unsupervised algorithm for training structured predictors that is discriminative, convex, and avoids the use of EM. The idea is to formulate an unsupervised version of structured learning methods, such as maximum margin Markov networks, that can be trained via semidefinite programming. The result is a discriminative training criterion for structured predictors (like hidden Markov models) that remains unsupervised and does not create local minima. To reduce training cost, we reformulate the training procedure to mitigate the dependence on semidefinite programming, and finally propose a heuristic procedure that avoids semidefinite programming entirely. Experimental results show that the convex discriminative procedure can produce better conditional models than conventional Baum-Welch (EM) training.
Summary. Robot localization is the problem of how to estimate a robot's pose within an objective frame of reference. Traditional localization requires knowledge of two key conditional probabilities: the motion and sensor models. These models depend critically on the specific robot as well as its environment. Building these models can be time-consuming, manually intensive, and can require expert intuitions. However, the models are necessary for the robot to relate its own subjective view of sensors and motors to the robot's objective pose. In this paper we seek to remove the need for human provided models. We introduce a technique for subjective localization, relaxing the requirement that the robot localize within a global frame of reference. Using an algorithm for action-respecting non-linear dimensionality reduction, we learn a subjective representation of pose from a stream of actions and sensations. We then extract from the data natural motion and sensor models defined for this new representation. Monte Carlo localization is used to track this representation of the robot's pose while executing new actions and receiving new sensor readings. We evaluate the technique in a synthetic image manipulation domain and with a mobile robot using vision and laser sensors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.