84 research outputs found
Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk
In this paper we present an algorithm to compute risk averse policies in
Markov Decision Processes (MDP) when the total cost criterion is used together
with the average value at risk (AVaR) metric. Risk averse policies are needed
when large deviations from the expected behavior may have detrimental effects,
and conventional MDP algorithms usually ignore this aspect. We provide
conditions for the structure of the underlying MDP ensuring that approximations
for the exact problem can be derived and solved efficiently. Our findings are
novel inasmuch as average value at risk has not previously been considered in
association with the total cost criterion. Our method is demonstrated in a
rapid deployment scenario, whereby a robot is tasked with the objective of
reaching a target location within a temporal deadline where increased speed is
associated with increased probability of failure. We demonstrate that the
proposed algorithm not only produces a risk averse policy reducing the
probability of exceeding the expected temporal deadline, but also provides the
statistical distribution of costs, thus offering a valuable analysis tool
Bimanual regrasping from unimanual machine learning
Abstract — While unimanual regrasping has been studied ex-tensively, either by regrasping in-hand or by placing the object on a surface, bimanual regrasping has seen little attention. The recent popularity of simple end-effectors and dual-manipulator platforms makes bimanual regrasping an important behavior for service robots to possess. We solve the challenge of bimanual regrasping by casting it as an optimization problem, where the objective is to minimize execution time. The optimization problem is supplemented by image processing and a unimanual grasping algorithm based on machine learning that jointly identify two good grasping points on the object and the proper orientations for each end-effector. The optimization algorithm exploits this data by finding the proper regrasp location and orientation to minimize execution time. Influenced by human bimanual manipulation, the algorithm only requires a single stereo image as input. The efficacy of the method we propose is demonstrated on a dual manipulator torso equipped with Barrett WAM arms and Barrett Hands. I
Motion planning for cooperative manipulators folding flexible planar objects
Abstract — Research on robotic manipulation has mostly avoided the grasping of highly deformable objects, although they account for a significant portion of everyday grasping tasks. In this paper we address the problem of using cooperative manipulators for folding tasks of cloth-like deformable objects, from a motion planning perspective. We demonstrate that complex deformable object models are unnecessary for robotic applications. Consequently, a simple object model is exploited to create a new algorithm capable of generating collision-free folding motions for two cooperating manipulators. The algorithm encompasses the essential properties of manipulator-independence, parameterized fold quality, and speed. Numerous experiments executed on a real and simulated dual-manipulator robotic torso demonstrates the method’s effectiveness. I
Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel
The proliferation of unmanned vehicles offers many opportunities for solving
environmental sampling tasks with applications in resource monitoring and
precision agriculture. Informative path planning (IPP) includes a family of
methods which offer improvements over traditional surveying techniques for
suggesting locations for observation collection. In this work, we present a
novel solution to the IPP problem by using a coregionalized Gaussian processes
to estimate a dynamic scalar field that varies in space and time. Our method
improves previous approaches by using a composite kernel accounting for
spatiotemporal correlations and at the same time, can be readily incorporated
in existing IPP algorithms. Through extensive simulations, we show that our
novel modeling approach leads to more accurate estimations when compared with
formerly proposed methods that do not account for the temporal dimension.Comment: Accepted to IROS 202
Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms with Robust Performance Constraints
In this paper we consider a stochastic deployment problem, where a robotic
swarm is tasked with the objective of positioning at least one robot at each of
a set of pre-assigned targets while meeting a temporal deadline. Travel times
and failure rates are stochastic but related, inasmuch as failure rates
increase with speed. To maximize chances of success while meeting the deadline,
a control strategy has therefore to balance safety and performance. Our
approach is to cast the problem within the theory of constrained Markov
Decision Processes, whereby we seek to compute policies that maximize the
probability of successful deployment while ensuring that the expected duration
of the task is bounded by a given deadline. To account for uncertainties in the
problem parameters, we consider a robust formulation and we propose efficient
solution algorithms, which are of independent interest. Numerical experiments
confirming our theoretical results are presented and discussed
Probabilistic Graph-Clear
Abstract — This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate. I
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