让机器人像人一样动态抓布，还能自适应材料变化。
Dynamic Cloth Manipulation Considering Variable Stiffness and Material Change Using Deep Predictive Model with Parametric Bias
- 用可变刚度机制+参数化偏置，提升布料操控灵活性。
- 实验证明能准确检测并响应布料物理属性变化。
- 适合研究柔性物体操控与智能机器人感知的学者。
柔性物体（如布料）的动态操控是机器人领域的重大挑战。尽管深度学习在仿真和部分实际机器人中已取得进展，但仍存在诸多未解问题。人类能以高速灵活地操控身体，并在材料更换后通过几次尝试快速适应新特性。本研究聚焦两点：(1) 利用可变刚度机制实现更动态的肢体控制；(2) 通过参数化偏置应对被操作对象材料变化。将这两种方法融入深度预测模型，实验表明，具备可变刚度机制的肌骨骼人形机器人 Musashi-W 能在仿真与真实环境中动态操控布料，并实时检测其物理属性变化。
原文摘要 · Abstract (English)
Dynamic manipulation of flexible objects such as fabric, which is difficult to modelize, is one of the major challenges in robotics. With the development of deep learning, we are beginning to see results in simulations and in some actual robots, but there are still many problems that have not yet been tackled. Humans can move their arms at high speed using their flexible bodies skillfully, and even when the material to be manipulated changes, they can manipulate the material after moving it several times and understanding its characteristics. Therefore, in this research, we focus on the following two points: (1) body control using a variable stiffness mechanism for more dynamic manipulation, and (2) response to changes in the material of the manipulated object using parametric bias. By incorporating these two approaches into a deep predictive model, we show through simulation and actual robot experiments that Musashi-W, a musculoskeletal humanoid with variable stiffness mechanism, can dynamically manipulate cloth while detecting changes in the physical properties of the manipulated object.
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