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.

Neuronal mechanism of state control in Drosophila melanogaster

The changes in internal states, such as fear, hunger and sleep affect behavioral responses in animals. In most of the cases, these state-dependent influences are “pleiotropic”: one state affects multiple sensory modalities and behaviors; “scalable”: the strengths and choices of such modulations differ depending on the imminence of demands; and “persistent”: once the state is switched on the effects last even after the internal demands are off. These prominent features of state-control enable animals to adjust their behavioral responses depending on their internal demands. Here, we studied the neuronal mechanisms of state-controls by investigating energy-deprived state (hunger state) and social-deprived state of fruit flies, Drosophila melanogaster, as prototypic models. To approach these questions, we developed two novel methods: a genetically based method to map sites of neuromodulation in the brain and optogenetic tools in Drosophila.

These methods, and genetic perturbations, reveal that the effect of hunger to alter behavioral sensitivity to gustatory cues is mediate by two distinct neuromodulatory pathways. The neuropeptide F (NPF) – dopamine (DA) pathway increases sugar sensitivity under mild starvation, while the adipokinetic hormone (AKH)- short neuropeptide F (sNPF) pathway decreases bitter sensitivity under severe starvation. These two pathways are recruited under different levels of energy demands without any cross interaction. Effects of both of the pathways are mediated by modulation of the gustatory sensory neurons, which reinforce the concept that sensory neurons constitute an important locus for state-dependent control of behaviors. Our data suggests that multiple independent neuromodulatory pathways are underlying pleiotropic and scalable effects of the hunger state.

In addition, using optogenetic tool, we show that the neural control of male courtship song can be separated into probabilistic/biasing, and deterministic/command-like components. The former, but not the latter, neurons are subject to functional modulation by social experience, supporting the idea that they constitute a locus of state-dependent influence. Interestingly, moreover, brief activation of the former, but not the latter, neurons trigger persistent behavioral response for more than 10 min. Altogether, these findings and new tools described in this dissertation offer new entry points for future researchers to understand the neuronal mechanism of state control.

Localization of sources of human evoked responses

The evoked response, a signal present in the electro-encephalogram when specific sense modalities are stimulated with brief sensory inputs, has not yet revealed as much about brain function as it apparently promised when first recorded in the late 1940's. One of the problems has been to record the responses at a large number of points on the surface of the head; thus in order to achieve greater spatial resolution than previously attained, a 50-channel recording system was designed to monitor experiments with human visually evoked responses.

Conventional voltage versus time plots of the responses were found inadequate as a means of making qualitative studies of such a large data space. This problem was solved by creating a graphical display of the responses in the form of equipotential maps of the activity at successive instants during the complete response. In order to ascertain the necessary complexity of any models of the responses, factor analytic procedures were used to show that models characterized by only five or six independent parameters could adequately represent the variability in all recording channels.

One type of equivalent source for the responses which meets these specifications is the electrostatic dipole. Two different dipole models were studied: the dipole in a homogeneous sphere and the dipole in a sphere comprised of two spherical shells (of different conductivities) concentric with and enclosing a homogeneous sphere of a third conductivity. These models were used to determine nonlinear least squares fits of dipole parameters to a given potential distribution on the surface of a spherical approximation to the head. Numerous tests of the procedures were conducted with problems having known solutions. After these theoretical studies demonstrated the applicability of the technique, the models were used to determine inverse solutions for the evoked response potentials at various times throughout the responses. It was found that reliable estimates of the location and strength of cortical activity were obtained, and that the two models differed only slightly in their inverse solutions. These techniques enabled information flow in the brain, as indicated by locations and strengths of active sites, to be followed throughout the evoked response.