Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating tasks such as food handling, garment sorting, or assistive dressing requires open problems of modeling, perceiving, planning, and control to be solved. Recent advances in data-driven approaches, together with classical control and planning, can provide viable solutions to these open challenges. In addition, with the development of better simulation environments, we can generate and study scenarios that allow for benchmarking of various approaches and gain better understanding of what theoretical developments need to be made and how practical systems can be implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey more than 100 relevant studies in this area and use it as the basis to discuss open problems. We adopt a learning perspective to unify the discussion over analytical and data-driven approaches, addressing how to use and integrate model priors and task data in perceiving and manipulating a variety of deformable objects.

感知和处理可变形物体是人类日常生活中不可或缺的一部分。食品处理、服装分类或辅助敷料等自动化任务需要解决建模、感知、规划和控制等开放性问题。数据驱动方法的最新进展，加上经典的控制和规划，可以为这些开放挑战提供可行的解决方案。此外，随着更好的模拟环境的发展，我们可以生成和研究允许对各种方法进行基准测试的场景，并更好地了解需要进行哪些理论发展以及如何实施和评估实际系统以提供灵活的、可扩展的，以及强大的解决方案。为此，我们调查了该领域的 100 多项相关研究，并以此为基础讨论开放性问题。我们采用学习的视角来统一对分析和数据驱动方法的讨论，解决如何使用和整合模型先验和任务数据来感知和操纵各种可变形对象。