Abstract: A method includes determining a first motion plan and a second motion plan based on inputs and determining a preference for the first motion plan relative to the second motion plan. The method also includes identifying one of the inputs as a sensitive input that causes the preference for the first motion plan over the second motion plan, and presenting, using a display, information that describes the first motion plan. The information includes an explanation indicating the sensitive input as a reason why the first motion plan is preferred over the second motion plan. The method also includes communicating and initiating the preferred motion plan.

Abstract: For manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32). The reinforcement is constrained (22) to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.

Abstract: For manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32). The reinforcement is constrained (22) to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.

Abstract: Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied.

Abstract: Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.

Abstract: Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied.

Abstract: Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.