DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1109/lra.2022.3193254
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively. We present DialFRED, a dialogue-enabled embodied instruction following benchmark based on the ALFRED benchmark. DialFRED allows an agent to actively ask questions to the human user; the additional information in the user's response is used by the agent to better complete its task. We release a human-annotated dataset with 53K task-relevant questions and answers and an oracle to answer questions. To solve DialFRED, we propose a questioner-performer framework wherein the questioner is pre-trained with the human-annotated data and fine-tuned with reinforcement learning. We make DialFRED publicly available and encourage researchers to propose and evaluate their solutions to building dialog-enabled embodied agents.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lra.2022.3193254
- OA Status
- green
- Cited By
- 37
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225675121