System and Method for Robust Robotic Manipulation using Chance Constrained Optimization

A robotic system, comprising:

• a robotic manipulator;
• a processor;
• and
• a memory having instructions stored thereon that, when executed by the processor, cause the robotic manipulator to:
• collect a digital representation of a task for manipulating an object from an initial state to a final state;
• solve a robust control problem to optimize a sequence of control forces to be applied by the robotic manipulator to change a state of the object from the initial state to the final state, wherein an evolution of the state of the object is governed by a stochastic complementarily system modeling the task of manipulating the object with a predefined probability wherein the robust control problem optimizes a cost function to generate the sequence of control forces performing the task subject to joint chance constraints including a first chance constraint on the state of the object being manipulated and a second chance constraint on stochastic complementarity constraints modeling manipulation of the object by the robotic manipulator;
• and
• control the manipulation of the object by applying the sequence of control forces changing the state of the object from the initial state to the final state.