Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills

Ben-ya Halevy; Dotan Di Castro; Yehudit Aperstein

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Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills

Ben-ya Halevy, Yehudit Aperstein, Dotan Di Castro

Robotics · Submitted 23 Jun 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments.

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arXiv ID2306.13630 (version 1)

Pith IDF7EEDZ6U, Pith's permanent record of this paper and its review

CitationBen-ya Halevy, Dotan Di Castro, Yehudit Aperstein. "Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills." arXiv:2306.13630 (2023). https://pith.science/paper/F7EEDZ6U

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,159 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.11 · human participants 0.05 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Reinforcement Learning has received wide interest due to its success in competitive games. Yet, its adoption in everyday applications is limited (e.g. industrial, home, healthcare, etc.). In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments. Our framework is comprised of three modules that together enable the agent to learn from previously collected data and generalize over it to solve long-horizon tasks. We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Reinforcement Learning has received wide interest due to its success in competitive games.	background 1.00	0.04	0.02	asserted 1.00
s1	Yet, its adoption in everyday applications is limited (e.g. industrial, home, healthcare, etc.).	background 0.57	0.06	0.02	asserted 1.00
s2	In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments.	contribution 1.00	0.81	0.39	asserted 0.99
s3	Our framework is comprised of three modules that together enable the agent to learn from previously collected data and generalize over it to solve long-horizon tasks.	method 0.88	0.73	0.12	asserted 1.00
s4	We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.	method 0.59	0.95	0.11	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.90.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Reinforcement Learning has received wide interest due to its success in competitive games. Yet, its adoption in everyday applications is limited (e.g. industrial, home, healthcare, etc.). In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments. Our framework is comprised of three modules that together enable the agent to learn from previously collected data and generalize over it to solve long-horizon tasks. We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 5 sentences without a confident label

Cites as background 2 papers

One proposed approach is offline RL (ORL; [ref] ), which avoids extensive exploration by allowing the agent to learn a policy from a static data set.

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems · INTRODUCTION · 0.89
Recently, [ref] introduced the DADS algorithm, which uses model-free RL methods to learn stochastic models for specific behaviors (skills).

Dynamics-Aware Unsupervised Discovery of Skills · RELATED WORK · 0.90

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