Robot motor skills can be learned through deep reinforcement learning (DRL)
by neural networks as state-action mappings. While the selection of state
observations is crucial, there has been a lack of quantitative analysis to
date. Here, we present a systematic saliency analysis that quantitatively
evaluates the relative importance of different feedback states for motor skills
learned through DRL. Our approach can identify the most essential feedback
states for locomotion skills, including balance recovery, trotting, bounding,
pacing and galloping. By using only key states including joint positions,
gravity vector, base linear and angular velocities, we demonstrate that a
simulated quadruped robot can achieve robust performance in various test
scenarios across these distinct skills. The benchmarks using task performance
metrics show that locomotion skills learned with key states can achieve
comparable performance to those with all states, and the task performance or
learning success rate will drop significantly if key states are missing. This
work provides quantitative insights into the relationship between state
observations and specific types of motor skills, serving as a guideline for
robot motor learning. The proposed method is applicable to differentiable
state-action mapping, such as neural network based control policies, enabling
the learning of a wide range of motor skills with minimal sensing dependencies.

使用深度强化学习，通过神经网络作为状态 - 动作映射，通过量化分析系统显著性分析来确定机器人学习的关键状态以实现迈步技能学习，其中包括平衡恢复，慢跑，奔跑，步态和奔跑。

识别学习运动技能的重要感觉反馈

Identifying Important Sensory Feedback for Learning Locomotion Skills

Among the different modalities to assess emotion, electroencephalogram (EEG),
representing the electrical brain activity, achieved motivating results over
the last decade. Emotion estimation from EEG could help in the diagnosis or
rehabilitation of certain diseases. In this paper, we propose a dual model
considering two different representations of EEG feature maps: 1) a sequential
based representation of EEG band power, 2) an image-based representation of the
feature vectors. We also propose an innovative method to combine the
information based on a saliency analysis of the image-based model to promote
joint learning of both model parts. The model has been evaluated on four
publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results
outperform results from state-of-the-art approaches for three of the proposed
datasets with a lower standard deviation that reflects higher stability. For
sake of reproducibility, the codes and models proposed in this paper are
available at this https URL.

本文提出了一种双模型方法，结合了基于序列的 EEG 幅值表示和基于图像的表示。采用基于显著性分析的图像模型，促进了两个模型部分的联合学习。在四个公开数据集上进行评估，结果表明本方法优于现有方法，在情感估计任务上取得了较高的稳定性。

一种基于显著性特征融合的 EEG 情感估计模型

A Saliency based Feature Fusion Model for EEG Emotion Estimation

Despite the remarkable advances in visual saliency analysis for natural scene
images (NSIs), salient object detection (SOD) for optical remote sensing images
(RSIs) still remains an open and challenging problem. In this paper, we propose
an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A
Global Context-aware Attention (GCA) module is proposed to adaptively capture
long-range semantic context relationships, and is further embedded in a Dense
Attention Fluid (DAF) structure that enables shallow attention cues flow into
deep layers to guide the generation of high-level feature attention maps.
Specifically, the GCA module is composed of two key components, where the
global feature aggregation module achieves mutual reinforcement of salient
feature embeddings from any two spatial locations, and the cascaded pyramid
attention module tackles the scale variation issue by building up a cascaded
pyramid framework to progressively refine the attention map in a coarse-to-fine
manner. In addition, we construct a new and challenging optical RSI dataset for
SOD that contains 2,000 images with pixel-wise saliency annotations, which is
currently the largest publicly available benchmark. Extensive experiments
demonstrate that our proposed DAFNet significantly outperforms the existing
state-of-the-art SOD competitors. this https URL

本文介绍了一种基于 Dense Attention Fluid network 的 Remote Sensing Images (RSIs) 显著性检测方法，提出了新的 Global Context-aware Attention (GCA) 和 cascaded pyramid attention 模块，并构建了包含 2,000 个图像的新型 RSI 数据集进行实验证明了该方法的明显优越性。