基于鲁棒示教学习的柔顺食品对象机器人操作
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration
AI总结：
本文针对柔顺食品对象机器人操作中示教不一致问题，提出一种融合RGB-D图像与触觉数据的鲁棒示教学习策略，可自动去除不一致示教并估计教师预期策略，实现复杂三维形状易碎食品的有效抓取。
摘要：
柔顺且可变形食品原材料的机器人操作，具有高生物变异性、复杂的三维几何形状以及机械结构和质地等特征，目前在海洋、农业和食品工业中需求巨大。这些行业中的许多任务由人类操作员手动完成，由于任务劳动密集且枯燥，操作员的执行表现出高度可变性，结果也不稳定。由于当前机器人学习策略的限制，为大多数复杂加工任务引入机器人自动化一直具有挑战性。人们期望一种由熟练操作员参与的、更具一致性的学习策略。本文针对机器人学习在不一致示教下所面临的问题进行研究。为此，我们提出了一种基于示教学习（Learning from Demonstration, LfD）的鲁棒学习策略，用于食品柔顺对象的机器人抓取。该方法融合RGB-D图像和触觉数据，以估计夹持器所需的位姿、夹持器手指配置以及施加在对象上的力，从而实现有效的机器人操作。在LfD训练过程中，夹持器位姿、手指配置、手指触觉值以及RGB-D图像均被保存。我们提出了一种LfD学习策略，能够自动去除不一致的示教，并估计示教者的预期策略。我们针对具有复杂三维形状的易碎柔顺食品对象验证并展示了该方法的性能。所提出的方法在上述工业领域具有广泛的潜在应用前景。
英文摘要：
The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are performed manually by human operators who, due to the laborious and tedious nature of their tasks, exhibit high variability in execution, with variable outcomes. The introduction of robotic automation for most complex processing tasks has been challenging due to current robot learning policies. A more consistent learning policy involving skilled operators is desired. In this paper, we address the problem of robot learning when presented with inconsistent demonstrations. To this end, we propose a robust learning policy based on Learning from Demonstration (LfD) for robotic grasping of food compliant objects. The approach uses a merging of RGB-D images and tactile data in order to estimate the necessary pose of the gripper, gripper finger configuration and forces exerted on the object in order to achieve effective robot handling. During LfD training, the gripper pose, finger configurations and tactile values for the fingers, as well as RGB-D images are saved. We present an LfD learning policy that automatically removes inconsistent demonstrations, and estimates the teacher's intended policy. The performance of our approach is validated and demonstrated for fragile and compliant food objects with complex 3D shapes. The proposed approach has a vast range of potential applications in the aforementioned industry sectors.
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。
