Navigation Among Movable Obstacles (NAMO) poses a challenge for traditional path-planning methods when obstacles block the path, requiring push actions to reach the goal. We propose a framework that enables movability-aware planning to overcome this challenge without relying on explicit obstacle placement. Our framework integrates a global Semantic Visibility Graph and a local Model Predictive Path Integral (SVG-MPPI) approach to efficiently sample rollouts, taking into account the continuous range of obstacle movability. A physics engine is adopted to simulate the interaction result of the rollouts with the environment, and generate trajectories that minimize contact force. In qualitative and quantitative experiments, SVG-MPPI outperforms the existing paradigm that uses only binary movability for planning, achieving higher success rates with reduced cumulative contact forces. Our code is available at: https://github.com/tud-amr/SVG-MPPI
Learning Goal-Directed Object Pushing in Cluttered Scenes With Location-Based Attention
Humanoid Robots Lab, University of Bonn, Germany（波恩大学人形机器人实验室）；School of Informatics, The University of Edinburgh, Edinburgh, UK（爱丁堡大学信息学院）；The Alan Turing Institute, London, UK（艾伦·图灵研究所）；The Lamarr Institute, Bonn, Germany（波恩兰马尔研究所）；The Center for Robotics, University of Bonn, Germany（波恩大学机器人中心）
Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；Department of Electrical and Computer Engineering, University of Waterloo（电气与计算机工程系，滑铁卢大学）；Department of Mechanical Engineering, University of Alberta（机械工程系，阿尔伯塔大学）
