SKT: 将状态感知关键点轨迹与视觉语言模型相结合用于机器人衣物操作
SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation
AI总结：
针对衣物变形多样导致机器人操作难、传统方法扩展性差的问题，提出融合状态感知关键点轨迹与VLM的统一方法，用合成数据集训练，提升了关键点检测精度与任务成功率，为相关应用奠基。
摘要：
由于衣物具有多样性和可变形的特性，实现衣物操作自动化对辅助机器人技术而言是一项重大挑战。传统方法通常需要为每种衣物类型配备独立的模型，这限制了可扩展性和适应性。相比之下，本文提出了一种利用视觉语言模型（VLM）的统一方法，以提升不同衣物类别的关键点预测效果。通过同时解析视觉信息和语义信息，我们的模型使机器人能够用单个模型处理不同的衣物状态。我们利用先进的仿真技术构建了一个大规模合成数据集，无需大量真实世界数据即可实现可扩展的训练。实验结果表明，基于VLM的方法显著提升了关键点检测精度和任务成功率，为机器人衣物操作提供了一种更灵活、更通用的解决方案。此外，本研究还凸显了VLM在单个框架内统一各类衣物操作任务的潜力，为其未来在家庭自动化和辅助机器人领域的更广泛应用铺平了道路。
英文摘要：
Automating garment manipulation poses a significant challenge for assistive robotics due to the diverse and deformable nature of garments. Traditional approaches typically require separate models for each garment type, which limits scalability and adaptability. In contrast, this paper presents a unified approach using vision-language models (VLMs) to improve keypoint prediction across various garment categories. By interpreting both visual and semantic information, our model enables robots to manage different garment states with a single model. We created a large-scale synthetic dataset using advanced simulation techniques, allowing scalable training without extensive real-world data. Experimental results indicate that the VLM-based method significantly enhances keypoint detection accuracy and task success rates, providing a more flexible and general solution for robotic garment manipulation. In addition, this research also underscores the potential of VLMs to unify various garment manipulation tasks within a single framework, paving the way for broader applications in home automation and assistive robotics for future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
