Sep 23, 2021 · In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion ...

In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and ...

In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and ...

Sep 23, 2021 · An algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) is proposed ...

@InProceedings{LIS1, title = {Discovering State and Action Abstractions for Generalized Task and Motion Planning}, author = {Aidan Curtis, Tom Silver ...

We present a strategy for designing and building very general robot manipulation systems involving the integration of a general-purpose task-and-motion planner ...

Experi- mentally, we show across four robotic planning en- vironments that our learned abstractions are able to quickly solve held-out tasks of longer horizons.

May 6, 2024 · This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning ...

In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and ...

This paper develops a novel framework for learning state and action abstractions that are explicitly optimized for both effective and efficient bilevel ...