AlfWorld

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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
• Embodied AI
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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

ALFRED (Action Learning From Realistic Environments and Directives) is a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications