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Bookmark or Share- Curriculum Learning via Task Selection for Embodied NavigationIn this work, we study the use of ACL for training long-horizon embodied AI tasks. We focus on ACL methods which generate their curriculum via task selection, i.e. meth-ods which select training tasks for the agent from a prede-fined dataset of existing tasks of varying complexity [20–22].
- Embodied AI WorkshopThe Embodied AI 2022 workshop will be held in conjunction with CVPR 2022. It will feature a host of invited talks covering a variety of topics in Embodied AI, many exciting challenges, a poster session, and panel discussions.
- Embodied AI WorkshopWe invite high-quality 2-page extended abstracts on embodied AI, especially in areas relevant to the themes of this year's workshop: Open-World AI for Embodied AI; Generative AI for Embodied AI; Embodied Mobile Manipulation; Language Model Planning; as well as themes related to embodied AI in general: Simulation Environments; Visual Navigation ...
- Learning to Navigate in Interactive Environments with the ... - Embodied AITo handle it, we propose a transformer encoder to learn a belief state which captures the long spatial-temporal dependencies of the aggregated observations in the memory. However, leveraging the trans-former architecture in the RL settings is highly unstable.
- Reduce, Reuse, Recycle: Modular Multi-Object Navigation - Embodied AIIn this work, we study how we can leverage agents trained on the simpler PointGoal task in the context of more com-plex long-horizon navigation tasks. We propose a modular approach called Modular-MON, where each module is re-sponsible for a specific task. In summary: i) we show that. [1] Bed.
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