Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry and dynamics are required to be known. This information is often extracted from CAD, limiting scalability and the ability to handle tasks with varying geometry. To reduce the need for a priori models, we propose a framework for estimating contact models online based on torque and position measurements. To do this, compliant contact models are used, connected in parallel to model multi-point contact and constraints such as a hinge. They are parameterized to be differentiable with respect to all of their parameters (rest position, stiffness, contact location), allowing the coupled robot/environment dynamics to be linearized or efficiently used in gradient-based optimization. These models are then applied for: offline gradient-based parameter fitting, online estimation via an extended Kalman filter, and online gradient-based MPC. The proposed approach is validated on two robots, showing the efficacy of sensorless contact estimation and the effects of online estimation on MPC performance.

翻译：诸如模型预测控制等控制技术能够实现接触丰富的操作，这类操作利用动态信息，同时维持摩擦极限和安全约束。然而，接触几何与动力学需要事先已知。这些信息通常从CAD中提取，限制了可扩展性以及处理几何变化任务的能力。为了减少对先验模型的需求，我们提出了一种基于力矩和位置测量在线估计接触模型的框架。为此，采用并联连接的柔性接触模型来模拟多点接触以及铰链等约束。这些模型被参数化，使其对所有参数（静息位置、刚度、接触位置）均可微，从而能够将耦合的机器人/环境动力学线性化或高效用于基于梯度的优化。随后，这些模型被应用于：离线基于梯度的参数拟合、通过扩展卡尔曼滤波器的在线估计，以及在线基于梯度的模型预测控制。所提方法在两个机器人上进行了验证，展示了无传感器接触估计的有效性以及在线估计对模型预测控制性能的影响。