A machine learning device according to the present invention learns an operation condition of a robot that stores a plurality of objects disposed on a carrier device in a container using a hand for grasping the objects. The machine learning device includes a state observation unit for observing the positions and postures of the objects and a state variable including at least one of cycle time to store the objects in the container and torque and vibration occurring when the robot grasps the objects during operation of the robot; a determination data obtaining unit for obtaining determination data for determining a margin of each of the cycle time, the torque, and the vibration against an allowance value; and a learning unit for learning the operation condition of the robot in accordance with a training data set constituted of a combination of the state variable and the determination data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning device for learning an operation condition of a robot that stores a plurality of objects disposed on a carrier device in a container using a hand for grasping the objects, the machine learning device comprising: a state observation unit for observing the positions and postures of the objects and a state variable including at least one of cycle time to store the objects in the container and torque and vibration occurring when the robot grasps the objects, during operation of the robot; a determination data obtaining unit for obtaining determination data for determining a margin of each of the cycle time, the torque, and the vibration against an allowance value; and a learning unit for learning the operation condition of the robot in accordance with a training data set constituted of a combination of the state variable and the determination data. 2. The machine learning device according to claim 1, wherein the cycle time is the time from when the robot begins storing the objects in the container until the robot completes storing the objects in the container. 3. The machine learning device according to claim 1, wherein the torque is calculated based on a current flowing through a motor for driving the robot. 4. The machine learning device according to claim 1, wherein the vibration is calculated based on an acceleration detected by an acceleration sensor provided in the hand. 5. The machine learning device according to claim 1, wherein the learning unit includes: a reward calculation unit for calculating a reward based on the determination data; and a value function update unit for updating a value function used for estimating the grasp order of the objects so as to reduce at least one of the cycle time, the torque, and the vibration, based on the reward. 6. The machine learning device according to claim 5, wherein the learning unit updates an action-value table that corresponds to the grasp order of the objects, based on the state variable of at least one of the cycle time, the torque, and the vibration and the reward. 7. The machine learning device according to claim 6, wherein the learning unit updates an action-value table that corresponds to at least one of the cycle time, the torque, and the vibration when another robot having the same configuration as the robot stores other objects in a container, based on a state variable of the other robot and the reward. 8. The machine learning device according to claim 5, wherein a reward calculation unit calculates the reward based on at least one of the cycle time, the torque, and the vibration. 9. The machine learning device according to claim 1, further comprising a decision determination unit for determining the grasp order of the objects based on a learning result by the learning unit in accordance with the training data set. 10. The machine learning device according to claim 1, wherein the machine learning device is connected to the robot through a network, and the state observation unit obtains a present state variable through the network. 11. The machine learning device according to claim 1, wherein the machine learning device is installed in a cloud server. 12. A machine learning method for learning an operation condition of a robot that stores a plurality of objects disposed on a carrier device in a container using a hand for grasping the objects, the machine learning method comprising the steps of: observing the positions and postures of the objects and a state variable including at least one of cycle time to store the objects in the container and torque and vibration occurring when the robot grasps the objects, during operation of the robot; obtaining determination data for determining a margin of each of the cycle time, the torque, and the vibration against an allowance value; and learning the operation condition of the robot in accordance with a training data set constituted of a combination of the state variable and the determination data.