RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics

• URL: http://arxiv.org/abs/2512.13660v1
• Date: Mon, 15 Dec 2025 18:52:43 GMT
• Title: RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics
• Authors: Enshen Zhou, Cheng Chi, Yibo Li, Jingkun An, Jiayuan Zhang, Shanyu Rong, Yi Han, Yuheng Ji, Mengzhen Liu, Pengwei Wang, Zhongyuan Wang, Lu Sheng, Shanghang Zhang,
• Abstract summary: We propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring.<n>RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning.<n>We present TraceSpatial-Bench, a challenging benchmark to evaluate spatial tracing.
• Score: 53.053660003572965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded reasoning compounded with complex spatial referring and real-world metric measurement. However, existing methods struggle with this compositional task. To this end, we propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring via a universal spatial encoder and a regression-supervised decoder to enhance scale awareness during supervised fine-tuning (SFT). Moreover, RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning (RFT) with metric-sensitive process rewards, supervising key intermediate perceptual cues to accurately generate spatial traces. To support SFT and RFT training, we introduce TraceSpatial, a large-scale dataset of 30M QA pairs, spanning outdoor/indoor/tabletop scenes and supporting complex reasoning processes (up to 9 steps). We further present TraceSpatial-Bench, a challenging benchmark filling the gap to evaluate spatial tracing. Experimental results show that RoboTracer surpasses baselines in spatial understanding, measuring, and referring, with an average success rate of 79.1%, and also achieves SOTA performance on TraceSpatial-Bench by a large margin, exceeding Gemini-2.5-Pro by 36% accuracy. Notably, RoboTracer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (UR5, G1 humanoid) in cluttered real-world scenes.

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