We present a novel approach to tackle the ObjectNav task for non-stationary
and potentially occluded targets in an indoor environment. We refer to this
task Portable ObjectNav (or P-ObjectNav), and in this work, present its
formulation, feasibility, and a navigation benchmark using a novel
memory-enhanced LLM-based policy. In contrast to ObjNav where target object
locations are fixed for each episode, P-ObjectNav tackles the challenging case
where the target objects move during the episode. This adds a layer of
time-sensitivity to navigation, and is particularly relevant in scenarios where
the agent needs to find portable targets (e.g. misplaced wallets) in
human-centric environments. The agent needs to estimate not just the correct
location of the target, but also the time at which the target is at that
location for visual grounding -- raising the question about the feasibility of
the task. We address this concern by inferring results on two cases for object
placement: one where the objects placed follow a routine or a path, and the
other where they are placed at random. We dynamize Matterport3D for these
experiments, and modify PPO and LLM-based navigation policies for evaluation.
Using PPO, we observe that agent performance in the random case stagnates,
while the agent in the routine-following environment continues to improve,
allowing us to infer that P-ObjectNav is solvable in environments with
routine-following object placement. Using memory-enhancement on an LLM-based
policy, we set a benchmark for P-ObjectNav. Our memory-enhanced agent
significantly outperforms their non-memory-based counterparts across object
placement scenarios by 71.76% and 74.68% on average when measured by Success
Rate (SR) and Success Rate weighted by Path Length (SRPL), showing the
influence of memory on improving P-ObjectNav performance. Our code and dataset
will be made publicly available.

我们提出了一种新颖的方法来解决室内环境中移动且可能被遮挡的目标的 ObjectNav 任务。我们称此任务为 Portable ObjectNav（或 P-ObjectNav），并在这项工作中，提出了它的形式，可行性，并使用一种新颖的基于增强记忆的 LLM 策略进行导航基准测试。