Bootstrapping bilinear models of robotic sensorimotor cascades

We consider the bootstrapping problem, which consists in learning a model of the agent's sensors and actuators starting from zero prior information, and we take the problem of servoing as a cross-modal task to validate the learned models. We study the class of bilinear dynamics sensors, in which the derivative of the observations are a bilinear form of the control commands and the observations themselves. This class of models is simple yet general enough to represent the main phenomena of three representative robotics sensors (field sampler, camera, and range-finder), apparently very different from one another. It also allows a bootstrapping algorithm based on hebbian learning, and that leads to a simple and bioplausible control strategy. The convergence properties of learning and control are demonstrated with extensive simulations and by analytical arguments

A group-theoretic approach to formalizing bootstrapping problems

The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets

Motion planning in observations space with learned diffeomorphism models

We consider the problem of planning motions in observations space, based on learned models of the dynamics that associate to each action a diffeomorphism of the observations domain. For an arbitrary set of diffeomorphisms, this problem must be formulated as a generic search problem. We adapt established algorithms of the graph search family. In this scenario, node expansion is very costly, as each node in the graph is associated to an uncertain diffeomorphism and corresponding predicted observations. We describe several improvements that ameliorate performance: the introduction of better image similarities to use as heuristics; a method to reduce the number of expanded nodes by preliminarily identifying redundant plans; and a method to pre-compute composite actions that make the search efficient in all directions

Learning data-derived vehicle motion models for use in localisation and mapping

Various solutions to the Simultaneous Localisation and Mapping (SLAM) problem have been proposed over the last 20 years. In particular, extending the fundamental solution of the SLAM problem has attracted a great deal of attention. Most extensions address shortcomings such as data association, computational complexity and improving predictions of a vehicle’s state. However, nearly all SLAM implementations still depend on analytical models to provide estimates for state transitions. Learning data-derived non-analytical models for use during localisation and mapping provides an alternative that could significantly improve estimates and increase the flexibility of models. A methodology to learn motion models without knowledge of the higher-order dynamics is therefore proposed using tapped delay-line neural networks (TDL-NN). Incorporating the learned Nth-order Markov model into a recursive Bayesian estimator requires that the learned model be assumed independent of the transitional model, forming a black box estimator. Both real-world and simulated training data were evaluated, along with changes to the input data’s format, to determine the best vehicle motion predictor. Furthermore, an evaluation methodology is defined to asses how well the models could learn each motion type. A comprehensive analysis of the one-forward prediction using various statistical measures was used to determine the most appropriate metric. The methodology evaluated the predictions at different levels of depth, providing supplementary information on the type of motions that are learnable. Outcomes of the experiments revealed that inherently learning a vehicle’s dynamics cannot be achieved using TDL-NNs. Currently the best that such an approach can learn is the delta between the vehicle’s states. Consequently, modifications are required to the learning algorithms as well as the input data’s format that will force the strategies to learn the higher-order dynamics.Dissertation (MEng)--University of Pretoria, 2018.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Bootstrapping sensorimotor cascades: a group-theoretic perspective

The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. in this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constrain of having “no prior information” can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commans. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-word range-finder and camera date from publicly available datasets