cs.ROOct 8, 2026

Towards Path-Creative Navigation: Robot Navigation through Embodied Interaction

Authors: Haoyu Xi, Siwei Cheng, Xiangyuan Liu, Brenda Li, Wei Zhang

Organizations: School of Computer Science, Shanghai Jiao Tong University, Shanghai, P. R. China · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, P. R. China · School of Mechanical and Power Engineering, Zhengzhou university, Zhengzhou,P.R.China · University of Waterloo

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Abstract

Autonomous navigation in cluttered and constrained environments typically assumes a fixed environment and searches only for paths within existing free space. However, a route can be blocked by an articulated structure, a movable object, or a pedestrian, and reaching the goal may therefore require appropriate embodied interaction with the environment. This paper formulates Path-Creative Navigation (PCN), a navigation paradigm in which the robot recovers free space by coordinating locomotion and embodied interaction, and addresses one class of PCN tasks with a mapless framework. The vision-LiDAR-odometry framework integrates local observations, obstacle geometry, and robot-state estimates through a unified navigation system, providing stable semantic and geometric information for navigation and interaction. The traversability-aware decision method utilizes visual and LiDAR distance information to determine whether a blockage is actionable and whether it can be safely bypassed, enabling the robot to interact only when necessary while supporting articulated structure pushing, movable object pushing, obstacle avoidance, and pedestrian requests. We design simulation scenarios and evaluate different methods in both simulation and two real-world tasks that cannot be completed by conventional navigation alone without embodied interaction. These results demonstrate that our framework achieves higher task completion than the baseline methods by coordinating navigation and interaction in cluttered and constrained environments. Code is available at:{https://anonymous.4open.science/r/path-creative-navigation/}.

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Fig. 2: Overview of the framework. Vision, depth, LiDAR, and odometry are integrated to obtain stable object targets ot,io_{\mathrm{t,i}} and local geometric observations for closed-loop interaction. The traversability-aware decision method jointly evaluates object actionability, Corridor⁡(ot,i)\operatorname{Corridor}(o_{\mathrm{t,i}}) , and NoBypass⁡(ot,i)\operatorname{NoBypass}(o_{\mathrm{t,i}}) to determine NeedInteract⁡(ot,i)\operatorname{NeedInteract}(o_{\mathrm{t,i}}) . The robot therefore retains navigation for fixed or bypassable obstacles and invokes the corresponding embodied interaction only when necessary. After the scene changes, traversability is reassessed and navigation resumes toward the goal.
Fig. 3: Objects in simulation. The simulated environments include doors of different sizes, colors, and types; boxes of varying colors and sizes; and chairs with different sizes and orientations.
Fig. 4: Experimental trajectories in simulation. Our method successfully performs the necessary interactions while treating non-blocking actionable objects as normal obstacles, avoiding unnecessary interactions and reaching the goal.
Table 4
Fig. 5: Average execution time (left) and trial-level pass rate (right) of the evaluated methods.
Fig. 6: Real-world embodied interaction. Interaction executions include articulated structure push and movable obstacle push. The pedestrian request action makes the robot play a verbal request and stop moving until NeedInteract⁡(ot,i)=0\operatorname{NeedInteract}(o_{\mathrm{t,i}})=0 .

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