SYSTEMS AND METHODS FOR DETERMINING A TYPE OF GRASP FOR A ROBOTIC END-EFFECTOR
Substantially as described and illustrated herein including devices, methods of operation for the systems or devices, articles of manufacture including processor-executable instructions, and a system including a robot.
1 . A system comprising:
a robot including an end-effector;
at least one processor communicatively coupled to the end-effector; and
at least one nontransitory processor-readable storage device communicatively coupled to the at least one processor and which stores processor-executable instructions which, when executed by the at least one processor, cause the at least one processor to:
cause the end-effector to perform an attempt to manipulate an item;
receive a position value for the end-effector from a position sensor;
receive a velocity value for the end-effector from a velocity sensor;
determine a grasp type of the item based at least in part on the received position value and the received velocity value; and
create a grasp type signal that includes information that represents the determined grasp type.
2 . The system of claim 1 , wherein, when executed, the instructions further cause the at least one processor to use deep learning techniques to extract features from the received position value and the received velocity value.
3 . The system of claim 1 , wherein, when executed, the instructions further cause the at least one processor to update at least one processor-readable storage device with at least one of the position value, the velocity value, or the grasp type.
4 . The system of claim 1 , wherein the received position value and the received velocity value represent positions of two or more parts of the end-effector relative to each other.
5 . The system of claim 1 , wherein, when executed, the instructions further cause the at least one processor to select a grasp type from at least one of: a failure to grasp the item, a grasp of a bag containing the item, or a grasp of a wrong part of the item.
6 . The system of claim 1 further comprising:
an operator interface communicatively coupled to the at least one processor; and
wherein, when executed, the processor-executable instructions further cause the at least one processor to:
receive, at the robot from the operator interface, operator generated processor-executable robot control instructions which, when executed, cause the robot to perform an action.
7 . The system of claim 1 wherein, when executed, the processor-executable instructions further cause the at least one processor to:
receive, at the robot, autonomous processor-executable robot control instructions which, when executed, cause the robot to perform an action.
8 . The system of claim 1 wherein, when executed, the processor-executable instructions further cause the at least one processor to:
receive autonomous robot control instructions which when executed causes the robot to move the item and release the item.
9 . The system of claim 8 wherein, when executed, the processor-executable instructions further cause the at least one processor to:
reject the autonomous robot control instructions based on the grasp type.
10 . The system of claim 1 wherein to determine the grasp type, when executed, the processor-executable instructions cause the at least one processor to:
select a first grasp type value for the grasp type when the velocity value is below a first threshold value and the position value is below a second threshold value; and
select a second grasp type value for the grasp type when the velocity value is below the first threshold value and the position value is above a third threshold value.
11 . A method of operation for a system including at least one processor and a robot including an end-effector in communication with the at least one processor, the method comprising:
causing, by the at least one processor, the end-effector to perform an attempt to manipulate an item;
receiving, by the at least one processor, a position value for the end-effector from a position sensor;
receiving, by the at least one processor, a velocity value for the end-effector from a velocity sensor;
determining, by the at least one processor, a grasp type of the item based at least in part on the received position value and the received velocity value; and
generating, by the at least one processor, a grasp type signal that includes information that represents the selected grasp type.
12 . The method of claim 11 , further comprising using deep learning techniques to extract features from the received position value and the received velocity value.
13 . The method of claim 11 , further comprising updating, by the at least one processor, at least one processor-readable storage device with at least one of the position value, the velocity value, or the grasp type.
14 . The method of claim 11 , wherein the received position value and the received velocity value represent positions of two or more parts of the end-effector relative to each other.
15 . The method of claim 11 , wherein selecting a grasp type comprises selecting a grasp type from at least one of: a failure to grasp the item, a grasp of a bag containing the item, or a grasp of a wrong part of the item.
16 . The method of claim 11 wherein the system further includes a manipulator physically coupled to the end-effector, the method further comprising:
causing, by the last least one processor, the end-effector to change a location via the manipulator.
17 . The method of claim 11 wherein the system further includes an operator interface in communication with the at least one processor, the method further comprising:
receiving, at the robot from the operator interface, operator generated processor-executable robot control instructions which, when executed, cause the robot to perform an action.
18 . The method of claim 11 further comprising:
receiving, at the robot, autonomous processor-executable robot control instructions which, when executed, cause the robot to perform an action.
19 . The method of claim 11 wherein the system further includes an observer interface in communicatively with the at least one processor; and, the method further comprises:
receiving, at the robot from the observer interface, observer generated processor-readable information that represents a pose for the end-effector.
20 . The method of claim 11 further comprising:
receiving, at the at least one processor, robot control instructions which when executed causes the robot to perform at least one action; and
rejecting, by the at least one processor, the robot control instructions which when executed causes the robot to perform at least one action.
20 . The method of claim 11 wherein determining, by the at least one processor, a grasp type based on the received position value and the received velocity value comprises:
selecting, by the at least one processor, a first grasp type value for the grasp type when the velocity value is below a first threshold value and the position value is below a second threshold value; or
selecting, by the at least one processor, a second grasp type value for the grasp type when the velocity value is below the first threshold value and the position value is above a third threshold value.