This paper investigates the multi-agent cooperative exploration problem,
which requires multiple agents to explore an unseen environment via sensory
signals in a limited time. A popular approach to exploration tasks is to
combine active mapping with planning. Metric maps capture the details of the
spatial representation, but are with high communication traffic and may vary
significantly between scenarios, resulting in inferior generalization.
Topological maps are a promising alternative as they consist only of nodes and
edges with abstract but essential information and are less influenced by the
scene structures. However, most existing topology-based exploration tasks
utilize classical methods for planning, which are time-consuming and
sub-optimal due to their handcrafted design. Deep reinforcement learning (DRL)
has shown great potential for learning (near) optimal policies through fast
end-to-end inference. In this paper, we propose Multi-Agent Neural Topological
Mapping (MANTM) to improve exploration efficiency and generalization for
multi-agent exploration tasks. MANTM mainly comprises a Topological Mapper and
a novel RL-based Hierarchical Topological Planner (HTP). The Topological Mapper
employs a visual encoder and distance-based heuristics to construct a graph
containing main nodes and their corresponding ghost nodes. The HTP leverages
graph neural networks to capture correlations between agents and graph nodes in
a coarse-to-fine manner for effective global goal selection. Extensive
experiments conducted in a physically-realistic simulator, Habitat, demonstrate
that MANTM reduces the steps by at least 26.40% over planning-based baselines
and by at least 7.63% over RL-based competitors in unseen scenarios.

在这篇论文中，我们提出了一种名为 MANTM（Multi-Agent Neural Topological Mapping）的方法，用于改善多智能体探索任务的效率和泛化能力，通过构建包含主要节点和对应虚拟节点的图形，以及使用图神经网络从粗到细的方式捕捉智能体与图节点之间的相关性进行全局目标选择。通过在模拟器 Habitat 中进行广泛实验，我们发现 MANTM 在未见过的场景中，相比于基于规划和基于强化学习的竞争方法，可以将步骤至少减少 26.40％和 7.63％。

多智能体探索的主动神经拓扑映射

Active Neural Topological Mapping for Multi-Agent Exploration

VoxML is a modeling language used to map natural language expressions into
real-time visualizations using commonsense semantic knowledge of objects and
events. Its utility has been demonstrated in embodied simulation environments
and in agent-object interactions in situated multimodal human-agent
collaboration and communication. It introduces the notion of object affordance
(both Gibsonian and Telic) from HRI and robotics, as well as the concept of
habitat (an object's context of use) for interactions between a rational agent
and an object. This paper aims to specify VoxML as an annotation language in
general abstract terms. It then shows how it works on annotating linguistic
data that express visually perceptible human-object interactions. The
annotation structures thus generated will be interpreted against the enriched
minimal model created by VoxML as a modeling language while supporting the
modeling purposes of VoxML linguistically.

本文介绍了 VoxML 作为一种建模语言，其通过常识语义知识将自然语言表达式映射到实时可视化中，涉及 HRI 和机器人技术中的目标特性概念，旨在将其作为注释语言，支持 VoxML 的建模目的。

VoxML 作为注释语言的抽象规范

An Abstract Specification of VoxML as an Annotation Language

Does progress in simulation translate to progress on robots? If one method
outperforms another in simulation, how likely is that trend to hold in reality
on a robot? We examine this question for embodied PointGoal navigation,
developing engineering tools and a research paradigm for evaluating a simulator
by its sim2real predictivity. First, we develop Habitat-PyRobot Bridge (HaPy),
a library for seamless execution of identical code on simulated agents and
robots, transferring simulation-trained agents to a LoCoBot platform with a
one-line code change. Second, we investigate the sim2real predictivity of
Habitat-Sim for PointGoal navigation. We 3D-scan a physical lab space to create
a virtualized replica, and run parallel tests of 9 different models in reality
and simulation. We present a new metric called Sim-vs-Real Correlation
Coefficient (SRCC) to quantify predictivity. We find that SRCC for Habitat as
used for the CVPR19 challenge is low (0.18 for the success metric), suggesting
that performance differences in this simulator-based challenge do not persist
after physical deployment. This gap is largely due to AI agents learning to
exploit simulator imperfections, abusing collision dynamics to 'slide' along
walls, leading to shortcuts through otherwise non-navigable space. Naturally,
such exploits do not work in the real world. Our experiments show that it is
possible to tune simulation parameters to improve sim2real predictivity (e.g.
improving $SRCC_{Succ}$ from 0.18 to 0.844), increasing confidence that
in-simulation comparisons will translate to deployed systems in reality.

本文针对机器人在虚拟仿真与现实世界中路线规划准确性的差异问题开展研究，提出了一种新的仿真评估工具 ——Habitat-PyRobot Bridge，并设计了一种衡量预测准确度的新指标 ——Sim-vs-Real Correlation Coefficient，实验结果表明调整仿真参数可以提高预测准确性，有助于将虚拟仿真结果转化为现实世界中的效果