2026-07-02 04:00 UTCOriginal source2 min readUpdated: 2026-07-02 08:34 UTC

Trajectory Learning with Graph Representations for Social Robot Navigation

This paper proposes an imitation learning framework that uses a graph-based auxiliary network to encode crowd interactions and a trajectory-level objective to capture spatiotemporal dynamics, outperforming existing baselines on simulation and real-world data.

SourcearXiv RoboticsAuthor: Berke Kartal, Burcu Kilic, Yigit Yildirim, Emre Ugur

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[Submitted on 21 Jun 2026]

Title:Trajectory Learning with Graph Representations for Social Robot Navigation

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Abstract:Autonomous mobile robots are expected to exhibit socially compliant navigation for minimizing pedestrian disturbance. While capturing social interactions and incorporating pedestrian motion estimations into decision-making are beneficial for compliance, prior methods fail to address both spatial and temporal characteristics present in real-world data. Reinforcement Learning offers high capability, but it requires hand-crafted reward functions that reduce social behavior to static criteria, limiting its ability to reproduce patterns that exist in real pedestrian behavior. Imitation Learning offers direct training from real-world data but lacks modeling of social interactions and suffers from error accumulation. To this end, we propose an imitation learning framework that leverages spatiotemporal dynamics for socially compliant navigation. To represent social context based on interactions, we introduce a graph-based auxiliary network that encodes crowd states by attending to pedestrians. In addition, we present a navigation module that captures temporal dynamics and mitigates error accumulations by incorporating encoded state predictions and employing a trajectory-level learning objective. Our framework outperforms established data-driven baselines on simulation and a real-world dataset across diverse social metrics.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2607.00028 [cs.RO]

(or arXiv:2607.00028v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.00028

arXiv-issued DOI via DataCite

Submission history

From: Berke Kartal [view email] [v1] Sun, 21 Jun 2026 23:57:54 UTC (4,031 KB)

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