Interactive Navigation With Adaptive Non-Prehensile Mobile Manipulation
Cunxi Dai, Xiaohan Liu, Koushil Sreenath, Zhongyu Li, Ralph Hollis
Abstract
This paper introduces a framework for interactive navigation through adaptive non-prehensile mobile manipulation. A key challenge in this process is to manipulate objects with unknown dynamics, which are difficult to infer from visual observation. To address this, we propose an adaptive dynamics model for common movable indoor objects via a learned <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(2)$</tex-math></inline-formula> dynamics representation. This model is integrated into a Model Predictive Path Integral (MPPI) controller to guide the robot's motion. Additionally, the learned dynamics help inform decision-making when navigating around objects that cannot be manipulated. Our approach is validated in both simulation and real-world scenarios, demonstrating its ability to accurately represent object dynamics and effectively manipulate various objects. We further highlight its success in the Navigation Among Movable Objects (NAMO) task by deploying the proposed framework on a dynamically balancing mobile robot, Shmoobot. Project website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cmushmoobot.github.io/AdaptivePushing/</uri>.
BibTeX
@inproceedings{ral2026_interactivenavig,
title = {Interactive Navigation With Adaptive Non-Prehensile Mobile Manipulation},
author = {Cunxi Dai and Xiaohan Liu and Koushil Sreenath and Zhongyu Li and Ralph Hollis},
booktitle = {RA-L 2026},
year = {2026}
}