Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Abstract—Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowl-edge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step towards addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Non-monotonic logical inference in ASP is used to generate a multi-nomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a Beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains. I

Slurs, roles and power

Slurring is a kind of hate speech that has various effects. Notable among these is variable offence. Slurs vary in offence across words, uses, and the reactions of audience members. Patterns of offence aren’t adequately explained by current theories. We propose an explanation based on the unjust power imbalance that a slur seeks to achieve. Our starting observation is that in discourse participants take on discourse roles. These are typically inherited from social roles, but only exist during a discourse. A slurring act is a speech-act that alters the discourse roles of the target and speaker. By assigning discourse roles the speaker unjustly changes the power balance in the dialogue. This has a variety of effects on the target and audience. We show how these notions explain all three types of offence variation. We also briefly sketch how a role and power theory can help explain silencing and appropriation. Explanatory power lies in the fact that offence is correlated with the perceived unjustness of the power imbalance created

Uncertainty Averse Pushing with Model Predictive Path Integral Control

Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.Comment: Humanoids 2017. Supplementary video: https://youtu.be/LjYruxwxkP

Characterization of glycan substrates accumulating in GM1 Gangliosidosis

Introduction: GM1 gangliosidosis is a rare autosomal recessive genetic disorder caused by the disruption of the GLB1 gene that encodes β-galactosidase, a lysosomal hydrolase that removes β-linked galactose from the non-reducing end of glycans. Deficiency of this catabolic enzyme leads to the lysosomal accumulation of GM1 and its asialo derivative GA1 in β-galactosidase deficient patients and animal models. In addition to GM1 and GA1, there are other glycoconjugates that contain β-linked galactose whose metabolites are substrates for β-galactosidase. For example, a number of N-linked glycan structures that have galactose at their non-reducing end have been shown to accumulate in GM1 gangliosidosis patient tissues and biological fluids. Objective: In this study, we attempt to fully characterize the broad array of GLB1 substrates that require GLB1 for their lysosomal turnover. Results: Using tandem mass spectrometry and glycan reductive isotope labeling with data-dependent mass spectrometry, we have confirmed the accumulation of glycolipids (GM1 and GA1) and N-linked glycans with terminal beta-linked galactose. We have also discovered a novel set of core 1 and 2 O-linked glycan metabolites, many of which are part of structurally-related isobaric series that accumulate in disease. In the brain of GLB1 null mice, the levels of these glycan metabolites increased along with those of both GM1 and GA1 as a function of age. In addition to brain tissue, we found elevated levels of both N-linked and O-linked glycan metabolites in a number of peripheral tissues and in urine. Both brain and urine samples from human GM1 gangliosidosis patients exhibited large increases in steady state levels for the same glycan metabolites, demonstrating their correlation with this disease in humans as well. Conclusions: Our studies illustrate that GLB1 deficiency is not purely a ganglioside accumulation disorder, but instead a broad oligosaccharidosis that include representatives of many β-linked galactose containing glycans and glycoconjugates including glycolipids, N-linked glycans, and various O-linked glycans. Accounting for all β-galactosidase substrates that accumulate when this enzyme is deficient increases our understanding of this severe disorder by identifying metabolites that may drive certain aspects of the disease and may also serve as informative disease biomarkers to fully evaluate the efficacy of future therapies