In robotic throwing, the release phase involves complex dynamic interactions due to object deformation and limited gripper opening speed, often resulting in inaccurate and nonrepeatable throws. While data-driven methods can be employed to compensate for the release uncertainty, the generalizability of learned models to unseen objects is not guaranteed, and object-specific fine-tuning with new data may be required. This fine-tuning process raises concerns about the scalability of such methods for dexterous throwing, where the robot needs to execute diverse motions for throwing various objects. Instead of case-by-case fine-tuning, we aim at designing throwing motion robust against release uncertainty. We encapsulate all uncertainties resulting from complex contact dynamics in a surrogate model of their resulting effect on gripper opening delay . We introduce the notion of tube acceleration to model the class of constant-acceleration motion in joint space that guarantees a release within the set of valid throwing configurations. We propose a convex relaxation of the primal optimization problem with a tight error bound and evaluate its performance in terms of reliability and efficiency. Results show that the approach offers run-time performance to allow online computation of throws on a 7-DoF robot arm. It achieves a high accuracy and success rate (97% for planar throws) at throwing a variety of complex objects, even when using a simple ballistic model for the object's flying dynamics.

中文翻译：

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管加速：针对释放不确定性的稳健灵巧投掷

在机器人投掷中，由于物体变形和有限的夹具打开速度，释放阶段涉及复杂的动态相互作用，通常会导致不准确和不可重复的投掷。虽然可以采用数据驱动的方法来补偿发布的不确定性，但不能保证学习模型对未见过的对象的通用性，并且可能需要使用新数据进行特定于对象的微调。这种微调过程引发了人们对此类灵巧投掷方法的可扩展性的担忧，其中机器人需要执行不同的动作来投掷各种物体。我们的目标不是针对具体情况进行微调，而是设计针对释放不确定性的稳健投掷运动。我们将复杂接触动力学产生的所有不确定性封装在其对影响的替代模型中夹具打开延迟。我们引入这个概念管加速度来模拟关节空间中的恒定加速度运动类别，保证在一组有效的投掷配置内释放。我们提出了具有严格误差界限的原始优化问题的凸松弛，并在可靠性和效率方面评估其性能。结果表明，该方法提供的运行时性能允许在线计算 7-DoF 机器人手臂上的投掷。即使使用简单的弹道模型来描述物体的飞行动力学，它在投掷各种复杂物体时也能实现较高的准确度和成功率（平面投掷为 97%）。