Realistic long-horizon tasks like image-goal navigation involve exploratory
and exploitative phases. Assigned with an image of the goal, an embodied agent
must explore to discover the goal, i.e., search efficiently using learned
priors. Once the goal is discovered, the agent must accurately calibrate the
last-mile of navigation to the goal. As with any robust system, switches
between exploratory goal discovery and exploitative last-mile navigation enable
better recovery from errors. Following these intuitive guide rails, we propose
SLING to improve the performance of existing image-goal navigation systems.
Entirely complementing prior methods, we focus on last-mile navigation and
leverage the underlying geometric structure of the problem with neural
descriptors. With simple but effective switches, we can easily connect SLING
with heuristic, reinforcement learning, and neural modular policies. On a
standardized image-goal navigation benchmark (Hahn et al. 2021), we improve
performance across policies, scenes, and episode complexity, raising the
state-of-the-art from 45% to 55% success rate. Beyond photorealistic
simulation, we conduct real-robot experiments in three physical scenes and find
these improvements to transfer well to real environments.

提出了一种名为 SLING 的新方法，致力于通过使用神经描述符和简单而有效的切换来改进现有的图像目标导航系统，在图像目标导航基准测试中取得了很大的成功，从而将成功率从 45％提高到 55％。

末端具身视觉导航

Last-Mile Embodied Visual Navigation

We describe SLING, a framework for parsing natural language into semantic
frames. SLING supports general transition-based, neural-network parsing with
bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU)
for output decoding. The parsing model is trained end-to-end using only the
text tokens as input. The transition system has been designed to output frame
graphs directly without any intervening symbolic representation. The SLING
framework includes an efficient and scalable frame store implementation as well
as a neural network JIT compiler for fast inference during parsing. SLING is
implemented in C++ and it is available for download on GitHub.

SLING 是一个用于将自然语言解析成语义框架的框架，使用双向 LSTM 输入编码和输出解码的转换式神经网络解析，可直接输出帧图而无需任何符号表示。