MARS: Multimodal Active Robotic Sensing for Articulated Characterization
<|reference_start|>MARS: Multimodal Active Robotic Sensing for Articulated Characterization: Precise perception of articulated objects is vital for empowering service robots. Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios. To address these limitations, we introduce MARS, a novel framework for articulated object characterization. It features a multi-modal fusion module utilizing multi-scale RGB features to enhance point cloud features, coupled with reinforcement learning-based active sensing for autonomous optimization of observation viewpoints. In experiments conducted with various articulated object instances from the PartNet-Mobility dataset, our method outperformed current state-of-the-art methods in joint parameter estimation accuracy. Additionally, through active sensing, MARS further reduces errors, demonstrating enhanced efficiency in handling suboptimal viewpoints. Furthermore, our method effectively generalizes to real-world articulated objects, enhancing robot interactions. Code is available at https://github.com/robhlzeng/MARS.<|reference_end|>
@article{zeng2024mars:,
    title={MARS: Multimodal Active Robotic Sensing for Articulated Characterization},
    author={Hongliang Zeng, Ping Zhang, Chengjiong Wu, Jiahua Wang, Tingyu Ye and
  Fang Li},
    journal={arXiv preprint arXiv:2407.01191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01191},
    primaryClass={cs.RO cs.AI cs.CV}
}