论文标题
学会通过可不同的物理模拟滑动未知对象
Learning to Slide Unknown Objects with Differentiable Physics Simulations
论文作者
论文摘要
我们提出了一种新技术，用于将未知对象从初始配置推向具有稳定性约束的目标配置。所提出的方法利用可区分物理模型的最新进展来学习推送物体的未知机械性能，例如它们的质量和摩擦系数的分布。提出的学习技术计算对象的预测姿势及其实际观察到的姿势之间的距离梯度，并利用该梯度来搜索减少现实差距的机械性能的值。提出的方法还用于优化策略，以有效地将对象推向所需的目标配置。使用真实机器人收集数据的真实对象的实验表明，所提出的方法可以从少量推动动作中识别异质对象的机械性能。
We propose a new technique for pushing an unknown object from an initial configuration to a goal configuration with stability constraints. The proposed method leverages recent progress in differentiable physics models to learn unknown mechanical properties of pushed objects, such as their distributions of mass and coefficients of friction. The proposed learning technique computes the gradient of the distance between predicted poses of objects and their actual observed poses and utilizes that gradient to search for values of the mechanical properties that reduce the reality gap. The proposed approach is also utilized to optimize a policy to efficiently push an object toward the desired goal configuration. Experiments with real objects using a real robot to gather data show that the proposed approach can identify the mechanical properties of heterogeneous objects from a small number of pushing actions.
