Event
Legal-logic-constrained motion planning validated on simulated robotic arm
1 reports · 1 sources
Dylan Waldner and other authors propose a "legal planning stack" that constrains a motion planner using defeasible deontic logic (DDL), and plans with the help of a learned world model, providing legal context, thereby intervening before illegal actions are executed. The stack is deployed in a scenario where a simulated robotic arm pushes blocks on a 3x3 grid. Results show that the legally constrained agent's rate of complying with norms is clearly higher than that of unconstrained agents; after modeling perceptual uncertainty, the compliance rate further improves. The authors state that legal reasoning is efficient at runtime, adjudication is auditable, and it can adapt to exogenous signals and endogenous rule changes.
Report timeline
- Constraining World-Model Planning with Legal Reasoning: Validation of a Defeasible Deontic Logic Planning Stack on a Simulated Robotic Arm
The paper proposes a legal planning stack that uses Defeasible Deontic Logic (DDL) to constrain a motion planner, leveraging a learned world model for planning and to supply legal context, thereby enabling ex ante intervention before an illegal action is executed. The stack was deployed in a scenario where a simulated robotic arm pushes blocks on a 3x3 grid. Results show that the legally constrained agent complied with norms at a markedly higher rate than the unconstrained one, and that modeling perceptual uncertainty further raised the compliance rate; legal reasoning ran efficiently at runtime, its rulings were auditable, and the stack adapted to exogenous signals and endogenous rule changes.
arXiv cs.RO ·