标题： Grasp-HGN：抓取意外情况 显示英文标题

标题： Grasp-HGN: Grasping the Unexpected

摘要： 对于经桡侧截肢者来说，机器人假手有望恢复进行日常活动的能力。 为了推进下一代假手控制设计，解决现有系统在实验室外的干扰因素下的鲁棒性不足以及在新环境中的泛化能力差的问题至关重要。 由于现有数据集中可交互的对象数量有限，而现实世界中物体种类几乎无限，当前抓握模型在未见过的物体上表现不佳，这影响了用户的独立性和生活质量。 为了解决这个问题：(i) 我们定义了语义投影，即模型对未见过物体类型的泛化能力，并表明传统模型如YOLO尽管训练准确率达到80%，但在未见过的物体上下降到15%。 (ii) 我们提出了Grasp-LLaVA，这是一种抓握视觉语言模型，能够根据物体的物理特性进行类人推理，从而推断合适的抓握类型，与最先进的抓握估计模型相比，在未见过的物体类型上取得了显著的50.2%的准确率，而后者为36.7%。 最后，为了弥合性能与延迟之间的差距，我们提出了混合抓握网络（HGN），这是一种边缘-云部署基础设施，能够在边缘快速进行抓握估计，并在云端进行精确推理作为备用方案，有效扩展了延迟与准确率的帕累托前沿。 带有置信度校准（DC）的HGN能够在边缘和云模型之间动态切换，使语义投影准确率提高5.6%（达到42.3%），在未见过的物体类型上的速度提升了3.5倍。 在真实世界样本混合测试中，其平均准确率达到86%（比仅使用边缘模型提高了12.2%），并且推理速度是Grasp-LLaVA单独使用的2.2倍。 显示英文摘要

摘要： For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. To advance next-generation prosthetic hand control design, it is crucial to address current shortcomings in robustness to out of lab artifacts, and generalizability to new environments. Due to the fixed number of object to interact with in existing datasets, contrasted with the virtually infinite variety of objects encountered in the real world, current grasp models perform poorly on unseen objects, negatively affecting users' independence and quality of life. To address this: (i) we define semantic projection, the ability of a model to generalize to unseen object types and show that conventional models like YOLO, despite 80% training accuracy, drop to 15% on unseen objects. (ii) we propose Grasp-LLaVA, a Grasp Vision Language Model enabling human-like reasoning to infer the suitable grasp type estimate based on the object's physical characteristics resulting in a significant 50.2% accuracy over unseen object types compared to 36.7% accuracy of an SOTA grasp estimation model. Lastly, to bridge the performance-latency gap, we propose Hybrid Grasp Network (HGN), an edge-cloud deployment infrastructure enabling fast grasp estimation on edge and accurate cloud inference as a fail-safe, effectively expanding the latency vs. accuracy Pareto. HGN with confidence calibration (DC) enables dynamic switching between edge and cloud models, improving semantic projection accuracy by 5.6% (to 42.3%) with 3.5x speedup over the unseen object types. Over a real-world sample mix, it reaches 86% average accuracy (12.2% gain over edge-only), and 2.2x faster inference than Grasp-LLaVA alone.