Hierarchical control for robotics has long been plagued by the need to have a
well defined interface layer to communicate between high-level task planners
and low-level policies. With the advent of LLMs, language has been emerging as
a prospective interface layer. However, this has several limitations. Not all
tasks can be decomposed into steps that are easily expressible in natural
language (e.g. performing a dance routine). Further, it makes end-to-end
finetuning on embodied data challenging due to domain shift and catastrophic
forgetting. We introduce our method -- Learnable Latent Codes as Bridges (LCB)
-- as an alternate architecture to overcome these limitations. \method~uses a
learnable latent code to act as a bridge between LLMs and low-level policies.
This enables LLMs to flexibly communicate goals in the task plan without being
entirely constrained by language limitations. Additionally, it enables
end-to-end finetuning without destroying the embedding space of word tokens
learned during pre-training. Through experiments on Language Table and Calvin,
two common language based benchmarks for embodied agents, we find that
\method~outperforms baselines (including those w/ GPT-4V) that leverage pure
language as the interface layer on tasks that require reasoning and multi-step
behaviors.

我们提出了一种名为 LC (学习型潜在代码桥接) 的新方法，用于在层次控制中克服自然语言的限制，并实现端到端微调，以在需要推理和多步行为的任务中优于仅使用纯语言作为接口层的基线。

从 LLMs 到行动：潜在代码作为层次机器人控制中的桥梁

From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot  Control

This paper presents an end-to-end neural mapping method for camera
localization, dubbed NeuMap, encoding a whole scene into a grid of latent
codes, with which a Transformer-based auto-decoder regresses 3D coordinates of
query pixels. State-of-the-art feature matching methods require each scene to
be stored as a 3D point cloud with per-point features, consuming several
gigabytes of storage per scene. While compression is possible, performance
drops significantly at high compression rates. Conversely, coordinate
regression methods achieve high compression by storing scene information in a
neural network but suffer from reduced robustness. NeuMap combines the
advantages of both approaches by utilizing 1) learnable latent codes for
efficient scene representation and 2) a scene-agnostic Transformer-based
auto-decoder to infer coordinates for query pixels. This scene-agnostic network
design learns robust matching priors from large-scale data and enables rapid
optimization of codes for new scenes while keeping the network weights fixed.
Extensive evaluations on five benchmarks show that NeuMap significantly
outperforms other coordinate regression methods and achieves comparable
performance to feature matching methods while requiring a much smaller scene
representation size. For example, NeuMap achieves 39.1% accuracy in the Aachen
night benchmark with only 6MB of data, whereas alternative methods require
100MB or several gigabytes and fail completely under high compression settings.
The codes are available at this https URL

本文介绍了一种名为 NeuMap 的端到端神经映射方法，其将整个场景编码为潜在代码网格，并使用基于 Transformer 的自动解码器回归查询像素的 3D 坐标，通过中间参数表示法和无需特征点的自动解码器结合优势，在保持网络权重固定的情况下能够学习大规模数据的鲁棒匹配特征，能够显著优于其他坐标回归方法，同时表示尺寸更小。