Estimation and inference for grasping and manipulation tasks using vision and kinesthetic sensors

This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters.

A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation.

Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub.

This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver.

Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.

Aseismic deformation in subduction megathrusts : central Andes and north-east Japan

We aim to characterize fault slip behavior during all stages of the seismic cycle in subduction megathrust environments with the eventual goal of understanding temporal and spatial variations of fault zone rheology, and to infer possible causal relationships between inter-, co- and post-seismic slip, as well as implications for earthquake and tsunami hazard. In particular we focus on analyzing aseismic deformation occurring during inter-seismic and post-seismic periods of the seismic cycle. We approach the problem using both Bayesian and optimization techniques. The Bayesian approach allows us to completely characterize the model parameter space by searching a posteriori estimates of the range of allowable models, to easily implement any kind of physically plausible a priori information and to perform the inversion without regularization other than that imposed by the parameterization of the model. However, the Bayesian approach computational expensive and not currently viable for quick response scenarios. Therefore, we also pursue improvements in the optimization inference scheme. We present a novel, robust and yet simple regularization technique that allows us to infer robust and somewhat more detailed models of slip on faults. We apply such methodologies, using simple quasi-static elastic models, to perform studies of inter- seismic deformation in the Central Andes subduction zone, and post-seismic deformation induced by the occurrence of the 2011 Mw 9.0 Tohoku-Oki earthquake in Japan. For the Central Andes, we present estimates of apparent coupling probability of the subduction interface and analyze its relationship to past earthquakes in the region. For Japan, we infer high spatial variability in material properties of the megathrust offshore Tohoku. We discuss the potential for a large earthquake just south of the Tohoku-Oki earthquake where our inferences suggest dominantly aseismic behavior.

Multi-model inference ranking and applications to physics at the Large Hadron Collider

In the measurement of the Higgs Boson decaying into two photons the parametrization of an appropriate background model is essential for fitting the Higgs signal mass peak over a continuous background. This diphoton background modeling is crucial in the statistical process of calculating exclusion limits and the significance of observations in comparison to a background-only hypothesis. It is therefore ideal to obtain knowledge of the physical shape for the background mass distribution as the use of an improper function can lead to biases in the observed limits. Using an Information-Theoretic (I-T) approach for valid inference we apply Akaike Information Criterion (AIC) as a measure of the separation for a fitting model from the data. We then implement a multi-model inference ranking method to build a fit-model that closest represents the Standard Model background in 2013 diphoton data recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). Potential applications and extensions of this model-selection technique are discussed with reference to CMS detector performance measurements as well as in potential physics analyses at future detectors.