Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible motion control to achieve safe yet high-performance operation. We propose a learning-supported model predictive force and motion control scheme that provides stochastic safety guarantees while adapting to changing situations. Gaussian processes are used to learn the uncertain relations that map the robot's states to the forces and moments. The model predictive controller uses these Gaussian process models to achieve precise motion and force control under stochastic constraint satisfaction. As the uncertainty only occurs in the static model parts -- the output equations -- a computationally efficient stochastic MPC formulation is used. Analysis of recursive feasibility of the optimal control problem and convergence of the closed loop system for the static uncertainty case are given. Chance constraint formulation and back-offs are constructed based on the variance of the Gaussian process to guarantee safe operation. The approach is illustrated on a lightweight robot in simulations and experiments.
翻译:许多机器人任务,如人-机器人相互作用或处理脆弱物体等,需要紧紧控制和限制外观力量和瞬间以及合理运动控制,以实现安全和高性能操作。我们提议一个学习支持的模型预测力和运动控制计划,在适应不断变化的形势的同时提供随机安全保障。高斯过程用来了解绘制机器人状态与力量和时刻图的不确定关系。模型预测控制器使用这些高斯过程模型,在随机性制约下实现精确的动作和武力控制。由于不确定性仅发生在静态模型部分 -- -- 输出方程式 -- -- 使用一种计算高效的随机式MPC配方。分析最佳控制问题和静态不确定性案例闭合环系统的递合性可行性。根据高斯过程的差异来构建机会制约配制和后退,以保证安全操作。该方法以模拟和实验中的轻量机器人为说明。</s>