用触觉在线优化分拆动作，让机械手自适应翻转未知物体。
Enhancing Adaptivity of Two-Fingered Object Reorientation Using Tactile-based Online Optimization of Deconstructed Actions
- 将复杂抓取动作分解为三类触觉驱动的基础动作
- 在未知接触和干扰下仍保持稳定翻转性能
- 适合需要高适应性的日常物体操作场景
物体翻转是机器人夹持器在受限环境中的一项关键任务。由于输出动作维度高、输入信息复杂（包括未知物体属性和非线性接触力），运动规划面临巨大挑战。传统方法通过降低自由度、限制接触形式或预先获取环境/物体信息来简化问题，显著削弱了适应性。为此，我们基于触觉感知，将复杂输出动作分解为三类基础类型：任务导向动作、约束导向动作和协调动作，并采用梯度优化实现在线动作调整以提升适应性。主要贡献包括简化接触状态感知、分解复杂夹持动作、实现在线动作优化以应对未知物体或环境约束。实验表明，该方法在多种常见物体上均有效，且不受环境接触条件影响；即使存在未知接触和非线性外部扰动，仍表现出鲁棒性能。
原文摘要 · Abstract (English)
Object reorientation is a critical task for robotic grippers, especially when manipulating objects within constrained environments. The task poses significant challenges for motion planning due to the high-dimensional output actions with the complex input information, including unknown object properties and nonlinear contact forces. Traditional approaches simplify the problem by reducing degrees of freedom, limiting contact forms, or acquiring environment/object information in advance, which significantly compromises adaptability. To address these challenges, we deconstruct the complex output actions into three fundamental types based on tactile sensing: task-oriented actions, constraint-oriented actions, and coordinating actions. These actions are then optimized online using gradient optimization to enhance adaptability. Key contributions include simplifying contact state perception, decomposing complex gripper actions, and enabling online action optimization for handling unknown objects or environmental constraints. Experimental results demonstrate that the proposed method is effective across a range of everyday objects, regardless of environmental contact. Additionally, the method exhibits robust performance even in the presence of unknown contacts and nonlinear external disturbances.
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