Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Yuki Shirai

Arvind Raghunathan

Devesh Jha

Diego Romeres

The present disclosure discloses a system and a method for controlling an operation of a robotic arm holding a tool for manipulating object. The method comprises collecting measurements of tactile sensors associated with the robotic arm, estimating, based on the collected measurements and constraints enforced by a Model Predictive Controller (MPC), a feedback signal indicative of a pose of the object, and executing the MPC configured to produce, based on the pose of the object, control commands for actuators of the robotic arm by optimizing a cost function minimizing a deviation of the pose of the object from a target pose of the object. The optimization of the cost function is subject to the constraints that constrain one or more forces acting on the object at one or more point of contacts to be within corresponding friction regions. The method further comprises controlling the actuators according to the control commands.

System and method for controlling an operation of a robotic arm

A manipulation controller is provided for reorienting an object by a manipulator of a robotic system. The manipulation controller includes an interface controller configured to acquire measurement data from sensors arranged on the robotic system, at least one processor, and a memory configured to store a computer-implemented method. The instructions of the method include acquiring measurement data from vision sensors and force sensors arranged on the robotic system, determining an input-output relation for the object based on a nonlinear static model representing input-output relationships between contact forces and movements of the object on the workbench, representing interaction between the object and the manipulator using complementarity constraints to capture the contact state between the object and the manipulator, formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench, estimating uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem, solving the bilevel optimization problem using the non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator.

System and method for robust pivoting for re-orienting parts during robotic assembly

The present disclosure provides a system and a method for controlling an operation of a manipulation system. The method comprises formulating an optimization problem based on a Stochastic Discrete-time Linear Complementarity Model (SDLCM) of a manipulation task, and a sample average approximation; solving the formulated optimization problem using an important-particle algorithm to compute an optimal state trajectory, an optimal feedforward control trajectory, an optimal complementarity variable trajectory, a state feedback gain, and a complementarity feedback gain; collecting, measurements indicative of a current state trajectory and a current complementarity variable trajectory; determining an online control input based on the optimal feedforward control trajectory, a deviation of the current state trajectory from the optimal state trajectory, a deviation of the current complementarity variable trajectory from the optimal complementarity variable trajectory, the state feedback gain, and the complementarity feedback gain; and controlling actuators of the manipulation system according to the online control input.

System and method for controlling an operation of a manipulation system

A robotic system for manipulating an object with a robotic manipulator is provided. The robotic system is configured to collect a digital representation of a task for manipulating the object; 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, where an evolution of the state of the object is governed by a stochastic complementarity system modeling the task with a predefined probability. 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 complementarily constraints modeling manipulation of the object. The robotic system is further configured to control the manipulation of the object based on the sequence of control forces.

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Cambridge, MA, United States of America

Yuki Shirai