Reinforcement learning is able to obtain generalized low-level robot policies
on diverse robotics datasets in embodied learning scenarios, and Transformer
has been widely used to model time-varying features. However, it still suffers
from the issues of low data efficiency and high inference latency. In this
paper, we propose to investigate the task from a new perspective of the
frequency domain. We first observe that the energy density in the frequency
domain of a robot's trajectory is mainly concentrated in the low-frequency
part. Then, we present the Fourier Controller Network (FCNet), a new network
that utilizes the Short-Time Fourier Transform (STFT) to extract and encode
time-varying features through frequency domain interpolation. We further
achieve parallel training and efficient recurrent inference by using FFT and
Sliding DFT methods in the model architecture for real-time decision-making.
Comprehensive analyses in both simulated (e.g., D4RL) and real-world
environments (e.g., robot locomotion) demonstrate FCNet's substantial
efficiency and effectiveness over existing methods such as Transformer, e.g.,
FCNet outperforms Transformer on multi-environmental robotics datasets of all
types of sizes (from 1.9M to 120M). The project page and code can be found
this https URL

通过在机器人轨迹的频域中提取和编码时变特征的短时傅里叶变换 (STFT)，傅里叶控制器网络 (FCNet) 实现了实时决策的并行训练和高效的循环推断，比传统方法如 Transformer 在各种大小的多环境机器人数据集上的效率和有效性更高。

傅立叶控制网络用于实时决策的具象学习

Fourier Controller Networks for Real-Time Decision-Making in Embodied  Learning

Visual representation learning hold great promise for robotics, but is
severely hampered by the scarcity and homogeneity of robotics datasets. Recent
works address this problem by pre-training visual representations on
large-scale but out-of-domain data (e.g., videos of egocentric interactions)
and then transferring them to target robotics tasks. While the field is heavily
focused on developing better pre-training algorithms, we find that dataset
choice is just as important to this paradigm's success. After all, the
representation can only learn the structures or priors present in the
pre-training dataset. To this end, we flip the focus on algorithms, and instead
conduct a dataset centric analysis of robotic pre-training. Our findings call
into question some common wisdom in the field. We observe that traditional
vision datasets (like ImageNet, Kinetics and 100 Days of Hands) are
surprisingly competitive options for visuo-motor representation learning, and
that the pre-training dataset's image distribution matters more than its size.
Finally, we show that common simulation benchmarks are not a reliable proxy for
real world performance and that simple regularization strategies can
dramatically improve real world policy learning.
this https URL

通过对机器人的预训练数据集进行集中分析，我们发现常规视觉数据集对于视觉 - 运动表示学习是竞争力强的选择，此外，预训练数据集的图像分布比其规模更为重要，同时简单的正规化策略可以显著改善真实世界策略学习。

关于视觉 - 动作预训练数据集的客观观察

An Unbiased Look at Datasets for Visuo-Motor Pre-Training

The grand aim of having a single robot that can manipulate arbitrary objects
in diverse settings is at odds with the paucity of robotics datasets. Acquiring
and growing such datasets is strenuous due to manual efforts, operational
costs, and safety challenges. A path toward such an universal agent would
require a structured framework capable of wide generalization but trained
within a reasonable data budget. In this paper, we develop an efficient system
(RoboAgent) for training universal agents capable of multi-task manipulation
skills using (a) semantic augmentations that can rapidly multiply existing
datasets and (b) action representations that can extract performant policies
with small yet diverse multi-modal datasets without overfitting. In addition,
reliable task conditioning and an expressive policy architecture enable our
agent to exhibit a diverse repertoire of skills in novel situations specified
using language commands. Using merely 7500 demonstrations, we are able to train
a single agent capable of 12 unique skills, and demonstrate its generalization
over 38 tasks spread across common daily activities in diverse kitchen scenes.
On average, RoboAgent outperforms prior methods by over 40% in unseen
situations while being more sample efficient and being amenable to capability
improvements and extensions through fine-tuning. Videos at
this https URL

通过语义增强和行为表征，我们开发了一个高效的系统（RoboAgent）来训练具备多任务操作技能的通用智能体，使用少量多样的多模态数据集，能够展示不同的技能并在新颖的场景中表现出很好的泛化性能。