arXiv cs.AI· Dylan Waldner, Yiannis Kantaros, Guido Governatori, Risto Miikkulainen, Amir Banifatemi·· 4 小时前AI 评分24
基于世界模型与法律推理的机器人规划立法框架研究（DDL 法律规划栈，JURIX 2026 论文）
Legislating World-Model-Based Planning with Legal Reasoning
AI 导读
一篇 arXiv 论文提出法律规划栈，用可废止道义逻辑（DDL）约束运动规划器，并借助学习到的世界模型在非法动作执行前实施事前治理。该栈部署在模拟机械臂 3x3 网格推方块任务上，立法后的智能体守规率显著更高，建模感知不确定性进一步提升，法律推理运行高效且裁决可审计。论文还量化了两个同构鸿沟：感知误差破坏 DDL 事实输入，同一法律可对应多种度量解释导致守规率差异巨大。
正文 · 原文
Abstract:As robotic systems grow more general, legal norms are needed to integrate them into society. This paper extends the isomorphism problem of aligning legal source texts with their encodings, and measures two key challenges to robot normative control: (1) the grounding isomorphism gap, where perception error grounds false atoms for legal reasoning, and (2) the ontological isomorphism gap, where one legal conclusion admits many faithful translations into planning constraints. The paper introduces a legal planning stack that employs Defeasible Deontic Logic (DDL) to constrain a motion planner. The stack leverages learned world models to plan and to provide legal context, enabling ex ante governance that intervenes before an illegal action is executed. It was deployed on a simulated robot arm pushing a cube across a 3x3 grid. The findings were (1) the legislated agent abided substantially more often than the non-legislated one, and modeling perception uncertainty lifted abidance even further, (2) the legal reasoning ran efficiently at runtime and its verdicts were auditable, and (3) the stack adapted to exogenous signals and endogenous rule changes. Both gaps were measured: (4) world model and probe error corrupted the factual input for the DDL reasoner, and (5) a single law admitted several faithful metric interpretations yielding drastically different abidance. Thus, ex ante legislation functions as intended, and closing these gaps with a standardized mapping from the law to runtime constraints and improved fact grounding from perception will yield robust laws that align robot behavior with society's norms. Project page: this https URL.
| Comments: | Project page: this https URL. Accepted at JURIX 2026 |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO) |
| Cite as: | arXiv:2609.15113 [cs.RO] |
| (or arXiv:2609.15113v3 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2609.15113 arXiv-issued DOI via DataCite |
Submission history
From: Dylan Waldner [view email]
[v1]
Mon, 14 Sep 2026 06:45:58 UTC (1,645 KB)
[v2]
Wed, 16 Sep 2026 21:58:06 UTC (1,645 KB)
[v3]
Mon, 26 Oct 2026 19:36:43 UTC (1,651 KB)
来源：arXiv cs.AI · arxiv.org