Learning Goal-Oriented Language-Guided Navigation with Self-Improving
  Demonstrations at Scale
Goal-oriented language-guided navigation requires robust exploration
capabilities for agents to navigate to specified goals in unknown environments
without step-by-step instructions. Existing methods tend to exclusively utilize
shortest-path trajectories, lacking effective exploration priors for training
navigation agents. To address the above challenges, we present SID, a
goal-oriented language-guided navigation learning approach with Self-Improving
Demonstrations. Specifically, SID learns an initial agent on the shortest-path
data sampled from environments and then leverages this agent to generate novel
exploration trajectories. The novel rollouts provide demonstrations with
stronger exploration strategies to train a better agent, which in turn produces
higher-quality agent demonstrations for the next round of training. We show
that this iterative self-improving pipeline readily scales to new environments,
and the resulting demonstrations can be transferred across a variety of
language-guided navigation tasks, elevating the performance ceiling in diverse
goal-oriented navigation tasks. Extensive experiments demonstrate that SID
significantly boosts the exploration capabilities and generalization of
navigation agents. The resulting agent achieves new state-of-the-art
performance on goal-oriented language-guided navigation tasks, including
REVERIE, SOON, notably achieving a 50.9% success rate on the unseen validation
splits of SOON, surpassing the prior leading approaches by a margin of 13.9%.
68db4317d2bf1f4b15ec7330
Songze Li
claimed_verified
2025-09-30T06:32:37.366Z
64acbbd51aee69ece03c6c0c
/avatars/604df1cabc5faeda55022ae4c1997e56.svg
Songze Li
user
SongzeLi
68db4317d2bf1f4b15ec7331
Zun Wang
68db4317d2bf1f4b15ec7332
Gengze Zhou
68db4317d2bf1f4b15ec7333
Jialu Li
68db4317d2bf1f4b15ec7334
Xiangyu Zeng
68db4317d2bf1f4b15ec7335
Limin Wang
68db4317d2bf1f4b15ec7336
Yu Qiao
68db4317d2bf1f4b15ec7337
Qi Wu
68db4317d2bf1f4b15ec7338
Mohit Bansal
68db4317d2bf1f4b15ec7339
Yi Wang
Learning Goal-Oriented Language-Guided Navigation with Self-Improving
  Demonstrations at Scale
Goal-oriented language-guided navigation requires robust exploration
capabilities for agents to navigate to specified goals in unknown environments
without step-by-step instructions. Existing methods tend to exclusively utilize
shortest-path trajectories, lacking effective exploration priors for training
navigation agents. To address the above challenges, we present SID, a
goal-oriented language-guided navigation learning approach with Self-Improving
Demonstrations. Specifically, SID learns an initial agent on the shortest-path
data sampled from environments and then leverages this agent to generate novel
exploration trajectories. The novel rollouts provide demonstrations with
stronger exploration strategies to train a better agent, which in turn produces
higher-quality agent demonstrations for the next round of training. We show
that this iterative self-improving pipeline readily scales to new environments,
and the resulting demonstrations can be transferred across a variety of
language-guided navigation tasks, elevating the performance ceiling in diverse
goal-oriented navigation tasks. Extensive experiments demonstrate that SID
significantly boosts the exploration capabilities and generalization of
navigation agents. The resulting agent achieves new state-of-the-art
performance on goal-oriented language-guided navigation tasks, including
REVERIE, SOON, notably achieving a 50.9% success rate on the unseen validation
splits of SOON, surpassing the prior leading approaches by a margin of 13.9%.
SID, a self-improving demonstration approach, enhances exploration and generalization in goal-oriented language-guided navigation tasks, achieving state-of-the-art performance.
goal-oriented language-guided navigation Self-Improving Demonstrations shortest-path trajectories exploration strategies iterative self-improving pipeline generalization REVERIE SOON