让机器人理解他人想法，实现更自然的社交导航。
Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
- 结合神经网络与符号推理，构建可推断他人信念的导航模型。
- 通过视角切换机制，精准估计他人隐藏意图与认知状态。
- 适合研究社交机器人、人机协作等需要共情能力的场景。
在人类共存的环境中导航，要求智能体在不确定性下推理他人的信念与意图。在序列决策框架中，自我中心导航可自然建模为马尔可夫决策过程（MDP）。但社交导航还需推理他人隐含信念，本质上构成部分可观测马尔可夫决策过程（POMDP），因智能体无法直接访问他人的心理状态。受心智理论与认知规划启发，我们提出：(1) 一种神经符号式模型强化学习架构，用于社交导航中的信念追踪；(2) 一种视角切换算子，利用结构化多智能体中的基于影响的抽象（IBA）方法进行信念估计。
原文摘要 · Abstract (English)
Navigating in environments alongside humans requires agents to reason under uncertainty and account for the beliefs and intentions of those around them. Under a sequential decision-making framework, egocentric navigation can naturally be represented as a Markov Decision Process (MDP). However, social navigation additionally requires reasoning about the hidden beliefs of others, inherently leading to a Partially Observable Markov Decision Process (POMDP), where agents lack direct access to others' mental states. Inspired by Theory of Mind and Epistemic Planning, we propose (1) a neuro-symbolic model-based reinforcement learning architecture for social navigation, addressing the challenge of belief tracking in partially observable environments; and (2) a perspective-shift operator for belief estimation, leveraging recent work on Influence-based Abstractions (IBA) in structured multi-agent settings.
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