Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs

Sridharan, Mohan; Wyatt, Jeremy L.; Dearden, Richard

doi:10.1016/j.artint.2010.04.022

Cited by 39 publications

(30 citation statements)

References 24 publications

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“…For each scene, the scene processing (SP)-POMDP plans the processing of regions of images of the scene using available algorithms. This hierarchical decomposition supports automatic belief propagation between the levels of the hierarchy and automatic model creation at each level [28], [29]. Thus, ASP-based inference operates at the (abstract) level of rooms, and POMDPs plan at the higher resolution of cells.…”

Section: B Planning Under Uncertainty With Partially Observable Markmentioning

confidence: 99%

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

2015

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Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowledge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Nonmonotonic logical inference in ASP is used to generate a multinomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains.

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Section: B Planning Under Uncertainty With Partially Observable Markmentioning

confidence: 99%

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

2015

Self Cite

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“…Sridharan et al propose a POMDP framework for planning a sequence of visual operators in a scenario where the robot converses with a human about objects on a table top [29]. The task involves the system to find objects referred by a human in various ways through natural language.…”

Section: Related Workmentioning

confidence: 99%

Active Visual Object Search in Unknown Environments Using Uncertain Semantics

et al. 2013

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In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.Index Terms-Active vision, semantic mapping, visual object search.

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“…in [13], who already looked at costs being dependent on the time of the day, but neither learned this from long-term experience nor where able to exploit the periodic nature explicitly. Since this early work, other researchers have incorporated probabilities into their planning domain with [14], [1], [15] being only three representatives of a group of works that employed (partially learned) probabilities to cope with the uncertainty of the world in robot planning. A topological map.…”

Section: Related Workmentioning

confidence: 99%

Now or later? Predicting and maximising success of navigation actions from long-term experience

Fentanes

Lacerda

Krajník

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-In planning for deliberation or navigation in realworld robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.

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Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs

Cited by 39 publications

References 24 publications

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Active Visual Object Search in Unknown Environments Using Uncertain Semantics

Now or later? Predicting and maximising success of navigation actions from long-term experience

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