対話可能な身体性指示追従エージェントのためのDialFREDベンチマーク
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
ALFREDを基に、エージェントが人間に能動的に質問できる身体性指示追従ベンチマークDialFREDを構築し、53KのQ&Aデータと質問者-実行者フレームワークを提案した。
著者: Xiaofeng Gao, Qiaozi Gao, Ran Gong, Kaixiang Lin, Govind Thattai, Gaurav S. Sukhatme
分類: cs.AI, cs.RO
原文アブストラクト
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.
