Qingxi Meng | Searcharxiv

arXiv2026

Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints

It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain perception constraints, such as monitoring people or multiple objects, for safety and reliability while executing its task. However, existing perception-aware approaches typically focus on low-degree-of-freedom (DoF) systems or only consider a single object in the context of high-DoF robots. This motivates us to consider the problem of perception-aware motion planning for high-DoF robots that accounts for multi-object monitoring constraints. We employ a scene graph representation of the environment, offering a great potential for incorporating long-horizon task and motion planning thanks to its rich semantic and spatial information. However, it does not capture perception-constrained information, such as the viewpoints the user prefers. To address these challenges, we propose MOPS-PRM, a roadmap-based motion planner, that integrates the perception cost of observing multiple objects or humans directly into motion planning for high-DoF robots. The perception cost is embedded to each object as part of a scene graph, and used to selectively sample configurations for roadmap construction, implicitly enforcing the perception constraints. Our method is extensively validated in both simulated and real-world experiments, achieving more than ~36% improvement in the average number of detected objects and ~17% better track rate against other perception-constrained baselines, with comparable planning times and path lengths.

cs.RO↗

arXiv2025

Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots

Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad range of robot perception objectives, including object detection, human activity recognition, and human face detection. For example, a service robot may need to continuously localize an object during manipulation, while an assistive robot may need to reliably perceive a human face or activity for interaction and safety. These tasks impose perception constraints on robot motion. However, solving motion and perception tasks simultaneously is challenging, as their requirements often conflict. Furthermore, robots must react quickly to environmental changes, while directly evaluating perception quality (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe operation. In this work, we address motion planning for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints in static and dynamic environments. Our solution is a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model (PS-PRM). Unlike existing active perception-, visibility-aware, or learning-based planners, our work jointly considers perception tasks and constraints when searching for a motion-planning solution. Our method uses a neural surrogate model to approximate perception scores, incorporates them into roadmap planning, and leverages GPU parallelism for efficient online replanning. We demonstrate that our planner outperforms RL- and trajectory-optimization-based baselines in static and dynamic environments in simulation and real-robot experiments.

cs.RO↗