We present a temporally layered architecture (TLA) for temporally adaptive
control with minimal energy expenditure. The TLA layers a fast and a slow
policy together to achieve temporal abstraction that allows each layer to focus
on a different time scale. Our design draws on the energy-saving mechanism of
the human brain, which executes actions at different timescales depending on
the environment's demands. We demonstrate that beyond energy saving, TLA
provides many additional advantages, including persistent exploration, fewer
required decisions, reduced jerk, and increased action repetition. We evaluate
our method on a suite of continuous control tasks and demonstrate the
significant advantages of TLA over existing methods when measured over multiple
important metrics. We also introduce a multi-objective score to qualitatively
assess continuous control policies and demonstrate a significantly better score
for TLA. Our training algorithm uses minimal communication between the slow and
fast layers to train both policies simultaneously, making it viable for future
applications in distributed control.

我们提出了一种时间分层框架 (TLA)，以最小化能量消耗进行时间自适应控制。 TLAlayer 了一个快速和一个慢速策略以实现时间抽象，使每层专注于不同的时间尺度。我们的设计借鉴了人脑的节能机制，根据环境要求在不同的时间尺度下执行动作。我们在一套连续控制任务上评估了我们的方法，并展示了 TLA 在多个重要指标上的显着优势，包括持续探索，所需决策减少，抖动减少和操作重复增加。我们还引入了一个多目标评分，以定性评估连续控制策略，并展示了 TLA 的得分显着更高。我们的训练算法在慢速和快速层之间使用最少的通信来同时训练两个策略，使其适用于分布式控制的未来应用。

高效连续控制的时间分层架构

Temporally Layered Architecture for Efficient Continuous Control

We explore the problem of automatically inferring the amount of kilocalories
used by human during physical activity from his/her video observation. To study
this underresearched task, we introduce Vid2Burn -- an omni-source benchmark
for estimating caloric expenditure from video data featuring both, high- and
low-intensity activities for which we derive energy expenditure annotations
based on models established in medical literature. In practice, a training set
would only cover a certain amount of activity types, and it is important to
validate, if the model indeed captures the essence of energy expenditure,
(e.g., how many and which muscles are involved and how intense they work)
instead of memorizing fixed values of specific activity categories seen during
training. Ideally, the models should look beyond such category-specific biases
and regress the caloric cost in videos depicting activity categories not
explicitly present during training. With this property in mind, Vid2Burn is
accompanied with a cross-category benchmark, where the task is to regress
caloric expenditure for types of physical activities not present during
training. An extensive evaluation of state-of-the-art approaches for video
recognition modified for the energy expenditure estimation task demonstrates
the difficulty of this problem, especially for new activity types at test-time,
marking a new research direction. Dataset and code are available at
this https URL

该研究探讨了如何从视频观察中自动推断人体在体力活动期间使用的卡路里量，并提出了一个可以同时处理高强度和低强度活动的数据集并通过评估表明该问题的困难性，需要进一步研究。