151 research outputs found
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
The All Different and Global Cardinality Constraints on Set, Multiset and Tuple Variables
We describe how the propagator for the All-Differentconstraintcanbegeneralizedtoprune variableswhosedomainsarenot justsimplefinitedomains. We show,forexample, howitcanbeused to propagate set variables, multisetvariablesandvariables whichrepresenttuplesofvalues. Wealsodescribehowthepropagatorfor theglobal cardinalityconstraint(whichisageneralization of the All-Different constraint) can be generalized in a similar way. Experiments show that such propagators can be beneficial in practice, especially when the domains are large
GOAT: GO to Any Thing
In deployment scenarios such as homes and warehouses, mobile robots are
expected to autonomously navigate for extended periods, seamlessly executing
tasks articulated in terms that are intuitively understandable by human
operators. We present GO To Any Thing (GOAT), a universal navigation system
capable of tackling these requirements with three key features: a) Multimodal:
it can tackle goals specified via category labels, target images, and language
descriptions, b) Lifelong: it benefits from its past experience in the same
environment, and c) Platform Agnostic: it can be quickly deployed on robots
with different embodiments. GOAT is made possible through a modular system
design and a continually augmented instance-aware semantic memory that keeps
track of the appearance of objects from different viewpoints in addition to
category-level semantics. This enables GOAT to distinguish between different
instances of the same category to enable navigation to targets specified by
images and language descriptions. In experimental comparisons spanning over 90
hours in 9 different homes consisting of 675 goals selected across 200+
different object instances, we find GOAT achieves an overall success rate of
83%, surpassing previous methods and ablations by 32% (absolute improvement).
GOAT improves with experience in the environment, from a 60% success rate at
the first goal to a 90% success after exploration. In addition, we demonstrate
that GOAT can readily be applied to downstream tasks such as pick and place and
social navigation
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