72,935 research outputs found
Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of
discrete-time planning problems, with hybrid state and control-spaces, as
factored transition systems. Factoring allows state transitions to be described
as the intersection of several constraints each affecting a subset of the state
and control variables. Robotic manipulation problems with many movable objects
involve constraints that only affect several variables at a time and therefore
exhibit large amounts of factoring. We develop a theoretical framework for
solving factored transition systems with sampling-based algorithms. The
framework characterizes conditions on the submanifold in which solutions lie,
leading to a characterization of robust feasibility that incorporates
dimensionality-reducing constraints. It then connects those conditions to
corresponding conditional samplers that can be composed to produce values on
this submanifold. We present two domain-independent, probabilistically complete
planning algorithms that take, as input, a set of conditional samplers. We
demonstrate the empirical efficiency of these algorithms on a set of
challenging task and motion planning problems involving picking, placing, and
pushing
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Physics-based Motion Planning with Temporal Logic Specifications
One of the main foci of robotics is nowadays centered in providing a great
degree of autonomy to robots. A fundamental step in this direction is to give
them the ability to plan in discrete and continuous spaces to find the required
motions to complete a complex task. In this line, some recent approaches
describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions
to guide sampling-based motion planning, with the aim of finding
dynamically-feasible motions that satisfy the temporal-logic task
specifications. The present paper proposes an LTL planning approach enhanced
with the use of ontologies to describe and reason about the task, on the one
hand, and that includes physics-based motion planning to allow the purposeful
manipulation of objects, on the other hand. The proposal has been implemented
and is illustrated with didactic examples with a mobile robot in simple
scenarios where some of the goals are occupied with objects that must be
removed in order to fulfill the task.Comment: The 20th World Congress of the International Federation of Automatic
Control, 9-14 July 201
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