Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications
Craig Innes, Yordan Hristov, Georgios Kamaras, Subramanian Ramamoorthy

TL;DR
This paper introduces an extension to ProbRobScene that automatically synthesizes uniform experiment designs from environment specifications, enabling efficient and reliable experimental setups in tabletop manipulation tasks.
Contribution
It presents a novel extension to probabilistic programming for automatic experiment design synthesis from environment specs.
Findings
Generates low-discrepancy experiment designs reliably.
Effective on tabletop manipulation environment snippets.
Enhances probabilistic programming capabilities.
Abstract
This paper presents an extension to the probabilistic programming language ProbRobScene, allowing users to automatically synthesize uniform experiment designs directly from environment specifications. We demonstrate its effectiveness on a number of environment specification snippets from tabletop manipulation, and show that our method generates reliably low-discrepancy designs.
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Taxonomy
TopicsMachine Learning and Algorithms · Formal Methods in Verification · Machine Learning and Data Classification
