Difference between revisions of "AlfWorld"
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* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | ||
| − | <b>AlfWorld</b>: Aligning Text and Embodied Environments for Interactive Learning. | + | <b>AlfWorld</b>: Aligning Text and Embodied Environments for Interactive Learning. a simulator that enables agents to learn abstract, text based policies in TextWorld (Côté et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER's simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding). The aligned environments allow agents to reason and learn high-level policies in an abstract space before solving embodied tasks through low-level actuation. |
<img src="https://github.com/alfworld/alfworld/raw/master/media/alfworld_teaser.png" width="600"> | <img src="https://github.com/alfworld/alfworld/raw/master/media/alfworld_teaser.png" width="600"> | ||
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| + | = ALFRED = | ||
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| + | <youtube>Iu2S1wWJ2T4</youtube> | ||
| + | <youtube>Iu2S1wWJ2T4</youtube> | ||
| + | <youtube>Iu2S1wWJ2T4</youtube> | ||
| + | <youtube>Iu2S1wWJ2T4</youtube> | ||
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Revision as of 07:50, 20 May 2023
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- AlfWorld: Aligning Text and Embodied Environments for Interactive Learning | M. Shridhar, X. Yuan, M. Côté, Y. Bisk, A. Trischler, M. Hausknecht - arXiv
- AlfWorld | GitHub
- Assistants ... Agents ... Negotiation ... LangChain
- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
AlfWorld: Aligning Text and Embodied Environments for Interactive Learning. a simulator that enables agents to learn abstract, text based policies in TextWorld (Côté et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER's simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding). The aligned environments allow agents to reason and learn high-level policies in an abstract space before solving embodied tasks through low-level actuation.
ALFRED
