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Found 6 papers, 1 papers with code
From Reals to Logic and Back: Inventing Symbolic Vocabularies, Actions, and Models for Planning from Raw Data
Empirical results in deterministic settings show that powerful abstract representations can be learned from just a handful of robot trajectories; the learned relational representations include but go beyond classical, intuitive notions of high-level actions; and that the learned models allow planning algorithms to scale to tasks that were previously beyond the scope of planning without hand-crafted abstractions.
Multi-Task Option Learning and Discovery for Stochastic Path Planning
This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems.
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability.
JEDAI: A System for Skill-Aligned Explainable Robot Planning
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts.
An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
We present a new approach for integrated task and motion planning in stochastic settings.
Anytime Integrated Task and Motion Policies for Stochastic Environments
We present a new approach for integrated task and motion planning in stochastic settings.
