MoMa-Pos: An Efficient Object-Kinematic-Aware Base Placement Optimization Framework for Mobile Manipulation

¹College of Computer Science, Chongqing University ²Xi’an Jiaotong Liverpool University ³ Shanghai Artificial Intelligence Laboratory

Abstract

In this work, we present MoMa-Pos, a frame work that optimizes base placement for mobile manipulators, focusing on navigation-manipulation tasks in environments with both rigid and articulated objects. Base placement is particularly critical in such environments, where improper positioning can severely hinder task execution if the object’s kinematics are not adequately accounted for. MoMa-Pos se lectively reconstructs the environment by prioritizing task relevant key objects, enhancing computational efficiency and ensuring that only essential kinematic details are processed. The framework leverages a graph-based neural network to predict object importance, allowing for focused modeling while minimizing unnecessary computations.

Additionally, MoMa Pos integrates inverse reachability maps with environmental kinematic properties to identify feasible base positions tailored to the specific robot model. Extensive evaluations demonstrate that MoMa-Pos outperforms existing methods in both real and simulated environments, offering improved efficiency, precision, and adaptability across diverse settings and robot models.

Framework of MoMa-Pos

MoMa-Pos consists of four phases, including object importance prediction for kinematic modeling，kinematic modeling of key objects, base placement optimization, and navigation to feasible base placement.
^⁎ In the first phase, key objects in the scene are prioritized to enable efficient kinematic modeling without processing every object.
^⁎ In the second phase, kinematic modeling is conducted for these prioritized objects, supporting the subsequent object-kinematic-aware base placement optimization.
^⁎ In the third phase, potential base placement areas are identified by considering both robot-specific constraints and environmental kinematics.

^⁎ In the last phase, the robot navigates to the optimal position, ensuring physical feasibility and adaptability for task execution

Experiments

In our experiments, MoMa-Pos (our method) was compared with three baseline methods across four distinct scenarios, with time and travel distance recorded for task execution. The results demonstrate that our method consistently achieves the highest task success rate with reduced time and cost.For additional supplementary materials, please see the video here.

Conclusion and Limitations

In this work, we introduce MoMa-Pos, a novel framework designed to optimize base placement for mobile manipulators, particularly in environments containing both rigid and articulated objects. MoMa-Pos addresses key challenges in navigation-manipulation tasks by selectively modeling taskrelevant objects, leveraging a graph-based neural network to enhance computational efficiency. By integrating inverse reachability maps and environmental kinematic properties, the framework enables precise base placement tailored to specific robot models, ensuring adaptability and physical feasibility across diverse environments.

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