Language provides a way to break down complex concepts into digestible
pieces. Recent works in robot imitation learning use language-conditioned
policies that predict actions given visual observations and the high-level task
specified in language. These methods leverage the structure of natural language
to share data between semantically similar tasks (e.g., "pick coke can" and
"pick an apple") in multi-task datasets. However, as tasks become more
semantically diverse (e.g., "pick coke can" and "pour cup"), sharing data
between tasks becomes harder, so learning to map high-level tasks to actions
requires much more demonstration data. To bridge tasks and actions, our insight
is to teach the robot the language of actions, describing low-level motions
with more fine-grained phrases like "move arm forward". Predicting these
language motions as an intermediate step between tasks and actions forces the
policy to learn the shared structure of low-level motions across seemingly
disparate tasks. Furthermore, a policy that is conditioned on language motions
can easily be corrected during execution through human-specified language
motions. This enables a new paradigm for flexible policies that can learn from
human intervention in language. Our method RT-H builds an action hierarchy
using language motions: it first learns to predict language motions, and
conditioned on this and the high-level task, it predicts actions, using visual
context at all stages. We show that RT-H leverages this language-action
hierarchy to learn policies that are more robust and flexible by effectively
tapping into multi-task datasets. We show that these policies not only allow
for responding to language interventions, but can also learn from such
interventions and outperform methods that learn from teleoperated
interventions. Our website and videos are found at
this https URL

通过使用语言动作，桥接任务和动作，实现从人类介入中学习的灵活策略，并展示了这些策略在多任务数据集上的鲁棒性和灵活性。

RT-H: 使用语言的动作层次结构

RT-H: Action Hierarchies Using Language

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical
results show that the network can autonomously learn to abstract sub-goals and
can self-develop an action hierarchy using internal dynamics in a challenging
continuous control task. Furthermore, we show that the self-developed
compositionality of the network enhances faster re-learning when adapting to a
new task that is a re-composition of previously learned sub-goals, than when
starting from scratch. We also found that improved performance can be achieved
when neural activities are subject to stochastic rather than deterministic
dynamics.

该研究提出一种新型的多时间尺度随机循环神经网络用于强化学习，可以自主学习抽象的子目标并自我开发行为层次结构，在挑战性的连续控制任务中确保了比起从头开始训练更快的再学习。同时，研究发现，当神经活动遵循随机动态时，可以实现更好的表现。