title: Bootstrapping Language-Guided Navigation Learning with Self-Refining
    Data Flywheel
summary: Creating high-quality data for training robust language-instructed agents is
  a long-lasting challenge in embodied AI. In this paper, we introduce a
  Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale
  navigational instruction-trajectory pairs by iteratively refining the data pool
  through the collaboration between two models, the instruction generator and the
  navigator, without any human-in-the-loop annotation. Specifically, SRDF starts
  with using a base generator to create an initial data pool for training a base
  navigator, followed by applying the trained navigator to filter the data pool.
  This leads to higher-fidelity data to train a better generator, which can, in
  turn, produce higher-quality data for training the next-round navigator. Such a
  flywheel establishes a data self-refining process, yielding a continuously
  improved and highly effective dataset for large-scale language-guided
  navigation learning. Our experiments demonstrate that after several flywheel
  rounds, the navigator elevates the performance boundary from 70% to 78% SPL on
  the classic R2R test set, surpassing human performance (76%) for the first
  time. Meanwhile, this process results in a superior generator, evidenced by a
  SPICE increase from 23.5 to 26.2, better than all previous VLN instruction
  generation methods. Finally, we demonstrate the scalability of our method
  through increasing environment and instruction diversity, and the
  generalization ability of our pre-trained navigator across various downstream
  navigation tasks, surpassing state-of-the-art methods by a large margin in all
  cases.
paper_authors._id: 675ad589659e5553260b6610
paper_authors.name: Zun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:54:52.948Z
paper_authors._id: 6622c710b0e5c5e3de8311c1
paper_authors.avatarUrl: /avatars/a824c150040731679bbd77762ca9d4eb.svg
paper_authors.fullname: Zun Wang
paper_authors.type: user
paper_authors.user: ZunWang
paper_authors._id: 675ad589659e5553260b6611
paper_authors.name: Jialu Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:54:45.022Z
paper_authors._id: 65d69eb0bcf1fed72325a052
paper_authors.avatarUrl: /avatars/5ef735c3137555bb52e512ae820e437c.svg
paper_authors.fullname: Jialu Li
paper_authors.type: user
paper_authors.user: jialuliluka
paper_authors._id: 675ad589659e5553260b6612
paper_authors.name: Yicong Hong
paper_authors._id: 675ad589659e5553260b6613
paper_authors.name: Songze Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:53:46.180Z
paper_authors._id: 6751a10224431db3bed1f701
paper_authors.avatarUrl: /avatars/06dcc5ba6cec8aff217a8bbc7a7d3b73.svg
paper_authors.fullname: Songze Li
paper_authors.type: user
paper_authors.user: CatCatCat36
paper_authors._id: 675ad589659e5553260b6614
paper_authors.name: Kunchang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:53:39.201Z
paper_authors._id: 61fb81006374891646732f37
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1643872995181-61fb81006374891646732f37.jpeg
paper_authors.fullname: Kunchang Li
paper_authors.type: user
paper_authors.user: Andy1621
paper_authors._id: 675ad589659e5553260b6615
paper_authors.name: Shoubin Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:54:02.398Z
paper_authors._id: 63ee8fb05f1300034de097fd
paper_authors.avatarUrl: /avatars/bceedb88927d8633948266503c2dd0b1.svg
paper_authors.fullname: Yu
paper_authors.type: user
paper_authors.user: Shoubin
paper_authors._id: 675ad589659e5553260b6616
paper_authors.name: Yi Wang
paper_authors._id: 675ad589659e5553260b6617
paper_authors.name: Yu Qiao
paper_authors._id: 675ad589659e5553260b6618
paper_authors.name: Yali Wang
paper_authors._id: 675ad589659e5553260b6619
paper_authors.name: Mohit Bansal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T13:54:19.115Z
paper_authors._id: 665d9d3a057f7c508f98c625
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/665d9d3a057f7c508f98c625/u1R9P9sJoAl4zEIcetbPy.jpeg
paper_authors.fullname: Mohit Bansal
paper_authors.type: user
paper_authors.user: mohitbansal
paper_authors._id: 675ad589659e5553260b661a
paper_authors.name: Limin Wang
paper_title: Bootstrapping Language-Guided Navigation Learning with Self-Refining
    Data Flywheel
paper_summary: Creating high-quality data for training robust language-instructed agents is
  a long-lasting challenge in embodied AI. In this paper, we introduce a
  Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale
  navigational instruction-trajectory pairs by iteratively refining the data pool
  through the collaboration between two models, the instruction generator and the
  navigator, without any human-in-the-loop annotation. Specifically, SRDF starts
  with using a base generator to create an initial data pool for training a base
  navigator, followed by applying the trained navigator to filter the data pool.
  This leads to higher-fidelity data to train a better generator, which can, in
  turn, produce higher-quality data for training the next-round navigator. Such a
  flywheel establishes a data self-refining process, yielding a continuously
  improved and highly effective dataset for large-scale language-guided
  navigation learning. Our experiments demonstrate that after several flywheel
  rounds, the navigator elevates the performance boundary from 70% to 78% SPL on
  the classic R2R test set, surpassing human performance (76%) for the first
  time. Meanwhile, this process results in a superior generator, evidenced by a
  SPICE increase from 23.5 to 26.2, better than all previous VLN instruction
  generation methods. Finally, we demonstrate the scalability of our method
  through increasing environment and instruction diversity, and the
  generalization ability of our pre-trained navigator across various downstream
  navigation tasks, surpassing state-of-the-art methods by a large margin in all
  cases.
paper_ai_summary: A self-refining data flywheel generates high-quality navigational instruction-trajectory pairs for training language-instructed agents, surpassing human performance in navigation tasks.
paper_ai_keywords: Self-Refining Data Flywheel SRDF instruction generator navigator data self-refining process SPL R2R test set SPICE VLN instruction generation scalability generalization ability