More than A Point: Capturing Uncertainty with Adaptive Affordance Heatmaps for Spatial Grounding in Robotic Tasks
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866908595191283712 |
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| author | Shao, Xinyu Tang, Yanzhe Xie, Pengwei Zhou, Kaiwen Zhuang, Yuzheng Quan, Xingyue Hao, Jianye Zeng, Long Li, Xiu |
| author_facet | Shao, Xinyu Tang, Yanzhe Xie, Pengwei Zhou, Kaiwen Zhuang, Yuzheng Quan, Xingyue Hao, Jianye Zeng, Long Li, Xiu |
| contents | Many language-guided robotic systems rely on collapsing spatial reasoning into discrete points, making them brittle to perceptual noise and semantic ambiguity. To address this challenge, we propose RoboMAP, a framework that represents spatial targets as continuous, adaptive affordance heatmaps. This dense representation captures the uncertainty in spatial grounding and provides richer information for downstream policies, thereby significantly enhancing task success and interpretability. RoboMAP surpasses the previous state-of-the-art on a majority of grounding benchmarks with up to a 50x speed improvement, and achieves an 82\% success rate in real-world manipulation. Across extensive simulated and physical experiments, it demonstrates robust performance and shows strong zero-shot generalization to navigation. More details and videos can be found at https://robo-map.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | More than A Point: Capturing Uncertainty with Adaptive Affordance Heatmaps for Spatial Grounding in Robotic Tasks Shao, Xinyu Tang, Yanzhe Xie, Pengwei Zhou, Kaiwen Zhuang, Yuzheng Quan, Xingyue Hao, Jianye Zeng, Long Li, Xiu Robotics Many language-guided robotic systems rely on collapsing spatial reasoning into discrete points, making them brittle to perceptual noise and semantic ambiguity. To address this challenge, we propose RoboMAP, a framework that represents spatial targets as continuous, adaptive affordance heatmaps. This dense representation captures the uncertainty in spatial grounding and provides richer information for downstream policies, thereby significantly enhancing task success and interpretability. RoboMAP surpasses the previous state-of-the-art on a majority of grounding benchmarks with up to a 50x speed improvement, and achieves an 82\% success rate in real-world manipulation. Across extensive simulated and physical experiments, it demonstrates robust performance and shows strong zero-shot generalization to navigation. More details and videos can be found at https://robo-map.github.io. |
| title | More than A Point: Capturing Uncertainty with Adaptive Affordance Heatmaps for Spatial Grounding in Robotic Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.10912 |