In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establish real-time feedback of multi-contact information. Then the interaction locations and forces are accommodated in the MPC framework via a spring contact model. Moreover, we achieved real-time control for a 7 degrees of freedom robot without any simplifying assumptions by employing a Differential-Dynamic-Programming algorithm. We achieved 6.8kHz, 1.9kHz, and 1.8kHz update rates of the MPC for 0, 1, and 2 contacts, respectively. This allows the robot to handle unexpected contacts in real time. Real-world experiments show the effectiveness of the proposed method in various scenarios.

翻译：在本文中，我们提出了一种模型预测控制（MPC）方法，用于实现交互式机器人任务，其中可能在未知位置发生多个接触。为应对此类场景，我们在MPC框架中显式构建了接触反馈回路。采用一种名为"带探索粒子的多接触粒子滤波器"（MCP-EP）的算法，以建立多接触信息的实时反馈。随后，通过弹簧接触模型将交互位置与力纳入MPC框架。此外，我们采用微分动态规划算法，在无需任何简化假设的条件下，实现了对七自由度机器人的实时控制。对于零个、一个和两个接触情形，我们分别实现了6.8kHz、1.9kHz和1.8kHz的MPC更新速率，这使得机器人能够实时处理意外接触。实物实验展示了所提方法在多种场景下的有效性。