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

Self-supervised methods have significantly closed the gap with end-to-end
supervised learning for image classification. In the case of human action
videos, however, where both appearance and motion are significant factors of
variation, this gap remains significant. One of the key reasons for this is
that sampling pairs of similar video clips, a required step for many
self-supervised contrastive learning methods, is currently done conservatively
to avoid false positives. A typical assumption is that similar clips only occur
temporally close within a single video, leading to insufficient examples of
motion similarity. To mitigate this, we propose SLIC, a clustering-based
self-supervised contrastive learning method for human action videos. Our key
contribution is that we improve upon the traditional intra-video positive
sampling by using iterative clustering to group similar video instances. This
enables our method to leverage pseudo-labels from the cluster assignments to
sample harder positives and negatives. SLIC outperforms state-of-the-art video
retrieval baselines by +15.4% on top-1 recall on UCF101 and by +5.7% when
directly transferred to HMDB51. With end-to-end finetuning for action
classification, SLIC achieves 83.2% top-1 accuracy (+0.8%) on UCF101 and 54.5%
on HMDB51 (+1.6%). SLIC is also competitive with the state-of-the-art in action
classification after self-supervised pretraining on Kinetics400.

通过使用迭代聚类对人体动作视频中的类似视频实例进行分组，以改进传统的爬虫视（intra-video）正样本采样，以使用聚类分配的伪标签进行样本较难的正样本和负样本的采用，SLIC 在人体动作视频中实现了优于国家视频检索基线的性能。