With recent developments in Embodied Artificial Intelligence (EAI) research,
there has been a growing demand for high-quality, large-scale interactive scene
generation. While prior methods in scene synthesis have prioritized the
naturalness and realism of the generated scenes, the physical plausibility and
interactivity of scenes have been largely left unexplored. To address this
disparity, we introduce PhyScene, a novel method dedicated to generating
interactive 3D scenes characterized by realistic layouts, articulated objects,
and rich physical interactivity tailored for embodied agents. Based on a
conditional diffusion model for capturing scene layouts, we devise novel
physics- and interactivity-based guidance mechanisms that integrate constraints
from object collision, room layout, and object reachability. Through extensive
experiments, we demonstrate that PhyScene effectively leverages these guidance
functions for physically interactable scene synthesis, outperforming existing
state-of-the-art scene synthesis methods by a large margin. Our findings
suggest that the scenes generated by PhyScene hold considerable potential for
facilitating diverse skill acquisition among agents within interactive
environments, thereby catalyzing further advancements in embodied AI research.
Project website: this http URL

通过与物理互动功能的先进集成，PhyScene 可有效生成具有可互动性的物理场景，与现有的场景合成方法相比，明显优势明显，有望在促进智能体在互动环境中获得丰富技能方面发挥巨大潜力。

PhyScene: 用于实体智能的可供物理交互的 3D 场景合成

PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI

Multimodal machine learning has gained significant attention in recent years
due to its potential for integrating information from multiple modalities to
enhance learning and decision-making processes. However, it is commonly
observed that unimodal models outperform multimodal models, despite the latter
having access to richer information. Additionally, the influence of a single
modality often dominates the decision-making process, resulting in suboptimal
performance. This research project aims to address these challenges by
proposing a novel regularization term that encourages multimodal models to
effectively utilize information from all modalities when making decisions. The
focus of this project lies in the video-audio domain, although the proposed
regularization technique holds promise for broader applications in embodied AI
research, where multiple modalities are involved. By leveraging this
regularization term, the proposed approach aims to mitigate the issue of
unimodal dominance and improve the performance of multimodal machine learning
systems. Through extensive experimentation and evaluation, the effectiveness
and generalizability of the proposed technique will be assessed. The findings
of this research project have the potential to significantly contribute to the
advancement of multimodal machine learning and facilitate its application in
various domains, including multimedia analysis, human-computer interaction, and
embodied AI research.

通过提出新的正则化项，促进多模态模型在决策过程中有效利用所有模态的信息，以缓解单模态主导性问题并提高多模态机器学习系统的性能。

多模态范式的归因规范化

Attribution Regularization for Multimodal Paradigms

We propose Embodied AI as the next fundamental step in the pursuit of
Artificial General Intelligence, juxtaposing it against current AI
advancements, particularly Large Language Models. We traverse the evolution of
the embodiment concept across diverse fields - philosophy, psychology,
neuroscience, and robotics - to highlight how EAI distinguishes itself from the
classical paradigm of static learning. By broadening the scope of Embodied AI,
we introduce a theoretical framework based on cognitive architectures,
emphasizing perception, action, memory, and learning as essential components of
an embodied agent. This framework is aligned with Friston's active inference
principle, offering a comprehensive approach to EAI development. Despite the
progress made in the field of AI, substantial challenges, such as the
formulation of a novel AI learning theory and the innovation of advanced
hardware, persist. Our discussion lays down a foundational guideline for future
Embodied AI research. Highlighting the importance of creating Embodied AI
agents capable of seamless communication, collaboration, and coexistence with
humans and other intelligent entities within real-world environments, we aim to
steer the AI community towards addressing the multifaceted challenges and
seizing the opportunities that lie ahead in the quest for AGI.

我们提出了 “具身人工智能” 作为追求 “人工通用智能” 的下一个基本步骤，并对其与当前人工智能进展，特别是大型语言模型进行对比。我们横跨哲学、心理学、神经科学和机器人技术等多个领域探讨了具身概念的演变，以突显具身人工智能如何与静态学习的经典范式有所区别。通过拓宽具身人工智能的范围，我们提出了一个基于认知架构的理论框架，强调感知、行动、记忆和学习作为具身代理的重要组成部分。该框架与弗里斯顿的主动推理原则相一致，为具身人工智能的发展提供了一个全面的方法。尽管人工智能领域取得了进展，但仍存在诸多挑战，如制定新的人工智能学习理论和创新先进硬件等。我们的讨论为未来的具身人工智能研究提供了基础性的指导方针。强调创造具身人工智能代理能够与人类和其他智能体在真实环境中实现无缝通信、协作和共存的重要性，我们旨在引导人工智能社群应对多方面挑战，并抓住在追求人工通用智能过程中出现的机遇。