From Legible to Inscrutable Trajectories: (Il)legible Motion Planning Accounting for Multiple Observers

2026-02-09

AI Summary

This work addresses motion planning for robots operating in multi-observer environments, where the robot must clearly convey its intent to benevolent observers while concealing it from adversarial ones, all under the constraints of each observer’s limited field of view. The paper introduces the Mixed-Motive and Limited-Visibility Legible Motion Planning (MMLO-LMP) problem, which, for the first time, jointly models differences in observer motives and visibility regions within a unified motion planning framework. To solve this problem, the authors develop DUBIOUS, a trajectory optimizer that employs multi-objective optimization to generate motion plans with differentiated levels of information exposure. Experimental results demonstrate that the proposed approach effectively balances predictability for benevolent observers against ambiguity for adversarial ones, achieving the desired dual objectives under realistic visibility constraints.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Tracking, profiling, and countermeasures against them
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, who could be either other humans or robots. A legible trajectory allows an observer to quickly and accurately predict an agent's intention. In adversarial environments, such as in military operations or games, it is important for a robot to not communicate its intentions to observers. An illegible trajectory leads an observer to incorrectly predict the agent's intention or delays when an observer is able to make a correct prediction about the agent's intention. However, in some environments there are multiple observers, each of whom may be able to see only part of the environment, and each of whom may have different motives. In this work, we introduce the Mixed-Motive Limited-Observability Legible Motion Planning (MMLO-LMP) problem, which requires a motion planner to generate a trajectory that is legible to observers with positive motives and illegible to observers with negative motives while also considering the visibility limitations of each observer. We highlight multiple strategies an agent can take while still achieving the problem objective. We also present DUBIOUS, a trajectory optimizer that solves MMLO-LMP. Our results show that DUBIOUS can generate trajectories that balance legibility with the motives and limited visibility regions of the observers. Future work includes many variations of MMLO-LMP, including moving observers and observer teaming.

Problem

• legible motion planning
• illegible trajectory
• multiple observers
• limited observability
• mixed-motive

Innovation

• legible motion planning
• illegible trajectories
• mixed-motive observers
• limited observability
• DUBIOUS optimizer