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Multi-Resolution POMDP Planning for Multi-Object Search in 3D
Robots operating in household environments must find objects on shelves,...
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Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments
We present PLUMES, a planner to localizing and collecting samples at the...
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Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe
Revision of the paper previously entitled "Learning a Machine for the De...
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Context Discovery for Model Learning in Partially Observable Environments
The ability to learn a model is essential for the success of autonomous ...
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Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
We present a slot-wise, object-based transition model that decomposes a ...
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Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments
Recent advances in data-driven models for grounded language understandin...
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pomdp_py: A Framework to Build and Solve POMDP Problems
In this paper, we present pomdp_py, a general purpose Partially Observab...
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Spatial Language Understanding for Object Search in Partially Observed Cityscale Environments
We present a system that enables robots to interpret spatial language as a distribution over object locations for effective search in partially observable cityscale environments. We introduce the spatial language observation space and formulate a stochastic observation model under the framework of Partially Observable Markov Decision Process (POMDP) which incorporates information extracted from the spatial language into the robot's belief. To interpret ambiguous, context-dependent prepositions (e.g. front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given environment context. We demonstrate the generalizability of our FoR prediction model and object search system through cross-validation over areas of five cities, each with a 40,000m^2 footprint. End-to-end experiments in simulation show that our system achieves faster search and higher success rate compared to a keyword-based baseline without spatial preposition understanding.
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