46,197 research outputs found
A simplistic approach to keyhole plan recognition
When applying plan recognition to Human - Computer Interaction, one
must cope with users exhibiting a large amount of reactive behaviour:
users that change tasks, or change strategies for achieving tasks.
Most current approaches to keyhole plan recognition do not address
this problem. We describe an application domain for plan recognition,
where users exhibit reactive rather than plan-based behaviour, and
where existing approaches to plan recognition do not perform well.
In order to enable plan recognition in this domain, we have developed
an extremely simplistic mechanism for keyhole plan recognition,
"intention guessing". The algorithm is based on descriptions of
observable behaviour, and is able to recognize certain instances of
plan failures, suboptimal plans and erroneous actions. At run-time,
the algorithm only keeps track of a limited number of the most recent
actions, which makes the algorithm "forgetful". This property makes
the algorithm suitable for domains where users frequently change
strategies
Structure and Complexity in Planning with Unary Operators
Unary operator domains -- i.e., domains in which operators have a single
effect -- arise naturally in many control problems. In its most general form,
the problem of STRIPS planning in unary operator domains is known to be as hard
as the general STRIPS planning problem -- both are PSPACE-complete. However,
unary operator domains induce a natural structure, called the domain's causal
graph. This graph relates between the preconditions and effect of each domain
operator. Causal graphs were exploited by Williams and Nayak in order to
analyze plan generation for one of the controllers in NASA's Deep-Space One
spacecraft. There, they utilized the fact that when this graph is acyclic, a
serialization ordering over any subgoal can be obtained quickly. In this paper
we conduct a comprehensive study of the relationship between the structure of a
domain's causal graph and the complexity of planning in this domain. On the
positive side, we show that a non-trivial polynomial time plan generation
algorithm exists for domains whose causal graph induces a polytree with a
constant bound on its node indegree. On the negative side, we show that even
plan existence is hard when the graph is a directed-path singly connected DAG.
More generally, we show that the number of paths in the causal graph is closely
related to the complexity of planning in the associated domain. Finally we
relate our results to the question of complexity of planning with serializable
subgoals
Critically fast pick-and-place with suction cups
Fast robotics pick-and-place with suction cups is a crucial component in the
current development of automation in logistics (factory lines, e-commerce,
etc.). By "critically fast" we mean the fastest possible movement for
transporting an object such that it does not slip or fall from the suction cup.
The main difficulties are: (i) handling the contact between the suction cup and
the object, which fundamentally involves kinodynamic constraints; and (ii)
doing so at a low computational cost, typically a few hundreds of milliseconds.
To address these difficulties, we propose (a) a model for suction cup contacts,
(b) a procedure to identify the contact stability constraint based on that
model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary
geometric paths under the identified contact stability constraint. We
experimentally validate the proposed pipeline on a physical robot system: the
cycle time for a typical pick-and-place task was less than 5 seconds, planning
and execution times included. The full pipeline is released as open-source for
the robotics community.Comment: 7 pages, 5 figure
Revision of Specification Automata under Quantitative Preferences
We study the problem of revising specifications with preferences for automata
based control synthesis problems. In this class of revision problems, the user
provides a numerical ranking of the desirability of the subgoals in their
specifications. When the specification cannot be satisfied on the system, then
our algorithms automatically revise the specification so that the least
desirable user goals are removed from the specification. We propose two
different versions of the revision problem with preferences. In the first
version, the algorithm returns an exact solution while in the second version
the algorithm is an approximation algorithm with non-constant approximation
ratio. Finally, we demonstrate the scalability of our algorithms and we
experimentally study the approximation ratio of the approximation algorithm on
random problem instances.Comment: 9 pages, 3 figures, 3 tables, in Proceedings of the IEEE Conference
on Robotics and Automation, May 201
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