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
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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
Embodied AI
Embodied AI is an emerging field where AI algorithms and agents learn through interactions with their environments from an egocentric perception similar to humans, rather than learning from datasets of images, videos or text curated primarily from the Internet. This involves working with real-world physical systems, such as robots. The embodiment hypothesis is the idea that “intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity”
