19,267 research outputs found
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
This work proposes a process for efficiently searching over combinations of
individual object 6D pose hypotheses in cluttered scenes, especially in cases
involving occlusions and objects resting on each other. The initial set of
candidate object poses is generated from state-of-the-art object detection and
global point cloud registration techniques. The best-scored pose per object by
using these techniques may not be accurate due to overlaps and occlusions.
Nevertheless, experimental indications provided in this work show that object
poses with lower ranks may be closer to the real poses than ones with high
ranks according to registration techniques. This motivates a global
optimization process for improving these poses by taking into account
scene-level physical interactions between objects. It also implies that the
Cartesian product of candidate poses for interacting objects must be searched
so as to identify the best scene-level hypothesis. To perform the search
efficiently, the candidate poses for each object are clustered so as to reduce
their number but still keep a sufficient diversity. Then, searching over the
combinations of candidate object poses is performed through a Monte Carlo Tree
Search (MCTS) process that uses the similarity between the observed depth image
of the scene and a rendering of the scene given the hypothesized pose as a
score that guides the search procedure. MCTS handles in a principled way the
tradeoff between fine-tuning the most promising poses and exploring new ones,
by using the Upper Confidence Bound (UCB) technique. Experimental results
indicate that this process is able to quickly identify in cluttered scenes
physically-consistent object poses that are significantly closer to ground
truth compared to poses found by point cloud registration methods.Comment: 8 pages, 4 figure
The Galileo PPS expert monitoring and diagnostic prototype
The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text
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Understanding analogical reasoning : viewpoints from psychology and related disciplines
Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning
Recursive Program Optimization Through Inductive Synthesis Proof Transformation
The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
Graph Based Reduction of Program Verification Conditions
Increasing the automaticity of proofs in deductive verification of C programs
is a challenging task. When applied to industrial C programs known heuristics
to generate simpler verification conditions are not efficient enough. This is
mainly due to their size and a high number of irrelevant hypotheses. This work
presents a strategy to reduce program verification conditions by selecting
their relevant hypotheses. The relevance of a hypothesis is determined by the
combination of a syntactic analysis and two graph traversals. The first graph
is labeled by constants and the second one by the predicates in the axioms. The
approach is applied on a benchmark arising in industrial program verification
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