SAVOR：面向机器人辅助取食的视觉-触觉感知技能可供性学习
SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition
AI总结：
针对机器人辅助取食中食物属性多变导致操作困难的问题，提出SAVOR方法，结合离线工具可供性学习与在线视觉-触觉感知的食物属性修正，实时预测技能可供性，较SOTA方法取食成功率提升13%。
摘要：
机器人辅助喂食需要可靠的取食动作，由于餐具与具有多样物理属性的食物之间存在复杂交互，这是一项极具挑战性的任务。食物属性的时间变化会进一步加剧这种交互的复杂性——例如，即使在用餐过程中，牛排也会随着冷却而变硬。为解决这一问题，我们提出了SAVOR，一种用于学习取食技能可供性的新方法——即评估某种操作技能（如穿刺、舀取）对于特定餐具-食物交互的适用程度。在我们的公式化定义中，技能可供性由工具可供性（餐具能做什么）和食物可供性（食物允许被如何操作）结合产生。工具可供性通过校准过程离线学习：让不同餐具与各类食物交互，以建模其功能能力。食物可供性由柔软度、湿度、黏度等物理属性表征，首先通过视觉条件语言模型进行常识推理初步推断，随后在交互过程中通过SAVOR-Net的在线多模态视觉-触觉感知进行动态修正。我们的方法整合这些离线与在线估计，实时预测技能可供性，使机器人能够为每种食物选择最合适的操作技能。在20种单品类食物和10种真实场景餐食上的评估显示，我们的方法相较于基于类别的当前最优（SOTA）方法（如用签子处理水果），将取食成功率提升了13%。这些结果凸显了建模交互驱动的技能可供性对于实现通用且高效的机器人辅助取食的重要性。项目网站：https://emprise.cs.cornell.edu/savor/
英文摘要：
Robot-assisted feeding requires reliable bite acquisition, a challenging task due to the complex interactions between utensils and food with diverse physical properties. These interactions are further complicated by the temporal variability of food properties-for example, steak becomes firm as it cools even during a meal. To address this, we propose SAVOR, a novel approach for learning skill affordances for bite acquisition-how suitable a manipulation skill (e.g., skewering, scooping) is for a given utensil-food interaction. In our formulation, skill affordances arise from the combination of tool affordances (what a utensil can do) and food affordances (what the food allows). Tool affordances are learned offline through calibration, where different utensils interact with a variety of foods to model their functional capabilities. Food affordances are characterized by physical properties such as softness, moisture, and viscosity, initially inferred through commonsense reasoning using a visually-conditioned language model and then dynamically refined through online multi-modal visuo-haptic perception using SAVOR-Net during interaction. Our method integrates these offline and online estimates to predict skill affordances in real time, enabling the robot to select the most appropriate skill for each food item. Evaluated on 20 single-item foods and 10 in-the-wild meals, our approach improves bite acquisition success rate by 13% over state-of-the-art (SOTA) category-based methods (e.g. use skewer for fruits). These results highlight the importance of modeling interaction-driven skill affordances for generalizable and effective robot-assisted bite acquisition. Website: https://emprise.cs.cornell.edu/savor/
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。
