Video2DoorTraversal: Push Door Traversal via Simulated Door Twins
Xincheng Tang, Yiji Chen, Youhan Xie, Wanyu Li, Zhengjie Shu, Lai Jiang, Wenkang Hu, Yitong Li, Jinchuang Zhang, Xibin Song, Ruigang Yang · N/A · 2026
Framework
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Summary
Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control. We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators.
Abstract Summary
Key Points
- Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control.
- We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators.
- Given one RGB video of a real door, DoorTwin reconstructs an instance-aligned, articulated, and simulation-ready door twin with realistic geometry and appearance.
- A simulation-in-the-loop agent converts the recovered articulation into a parameterized skill program and iteratively refines failed rollouts to generate physically executable demonstrations.
- These demonstrations are used to train ArticuACT, a dual-depth policy that predicts coordinated base, arm, and gripper commands using robot-centric camera conditioning and interaction-aware supervision.
Method
As illustrated in Fig. 2 , our framework transforms a single RGB video of a real door into a deployable quadruped loco-manipulation policy through three stages. First, DoorTwin reconstructs a metrically aligned, articulated, and simulation-ready door asset.
Second, a simulation-in-the-loop agent synthesizes and iteratively refines parameterized skill programs, from which successful domain-randomized expert trajectories are collected.
Experiments
For door asset generation, we capture a single RGB video of each target door using a smartphone camera. The hardware robot platform consists of a Unitree A2-W wheel-legged base equipped with a Unitree Z1 arm. Two Intel RealSense D435 cameras provide onboard perception: one is mounted on the robot head for global scene observation, while the other is mounted near the gripper for close-range interaction sensing.
The onboard computer of the A2-W executes the low-level base controller.
Sources and Demonstrations
Figures are reproduced from Tang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.
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