Valencia Angel J, Payeur Pierre
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.
Front Robot AI. 2020 Dec 23;7:600584. doi: 10.3389/frobt.2020.600584. eCollection 2020.
Modeling deformable objects is an important preliminary step for performing robotic manipulation tasks with more autonomy and dexterity. Currently, generalization capabilities in unstructured environments using analytical approaches are limited, mainly due to the lack of adaptation to changes in the object shape and properties. Therefore, this paper proposes the design and implementation of a data-driven approach, which combines machine learning techniques on graphs to estimate and predict the state and transition dynamics of deformable objects with initially undefined shape and material characteristics. The learned object model is trained using RGB-D sensor data and evaluated in terms of its ability to estimate the current state of the object shape, in addition to predicting future states with the goal to plan and support the manipulation actions of a robotic hand.
对可变形物体进行建模是使机器人更自主、灵活地执行操作任务的重要前期步骤。目前，使用分析方法在非结构化环境中的泛化能力有限，主要原因是缺乏对物体形状和属性变化的适应性。因此，本文提出了一种数据驱动方法的设计与实现，该方法结合了图上的机器学习技术，以估计和预测形状和材料特性最初未定义的可变形物体的状态和过渡动态。所学习的物体模型使用RGB-D传感器数据进行训练，并根据其估计物体形状当前状态的能力进行评估，此外还根据预测未来状态以规划和支持机器人手的操作动作的目标进行评估。
