Apparatus and methods for control of robot actions based on corrective user inputs

Patent metadata

Field	Value
Publication number	US-9358685-B2
Application number	US-201414171762-A
Country	US
Kind code	B2
Filing date	Feb 3, 2014
Priority date	Feb 3, 2014
Publication date	Jun 7, 2016
Grant date	Jun 7, 2016

Abstract

Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

First claim

What is claimed is:

1. A method for controlling actions of robots, the method comprising:
identifying, at a device that includes a processor, a first context-variable value for a context variable detected by a robot at a first sensory-detection time;
accessing, at the device, a policy comprising one or more parameters configured to map the context variable to a robot action variable;
determining that a first robot action characterized by a first value of the robot action variable was performed at an action time in response to detection of the first context-variable value, the first robot action being in accordance with application of the policy;
determining that a user input was received at an input time corresponding to the action time, wherein user input data derived from the user input at least partly defines a corrective command that specifies a corrective robot action for physical performance, the user input being indicative of at least partial dissatisfaction with the first robot action;
modifying the policy based on the corrective command and the first context-variable value; and
causing the modified policy to be used to:
determine a second robot action characterized by a second value of the robot action variable based on a second context-variable value for the context variable detected at a second sensory-detection time; and
initiate performance of the second robot action in accordance with the second value of the robot action variable.

2. The method of claim 1 , further comprising:
identifying a third context-variable value for the context variable, the third context-variable value being detected at a third sensory-detection time that is after the second sensory-detection time;
determining that the robot performed a third action in response to the third context-variable value, the third action being in accordance with application of the accessed policy; and
inferring that the third action was satisfactory based on a lack of input data east partly defining a corrective command corresponding to the third action;
wherein the modification of the policy is further based on the third context-variable value.

3. The method of claim 1 , further comprising:
identifying initial user input data derived from an initial user input received, the initial user input data at least partly defining an initial command that specifies an initial robot action for the robot to physically perform;
identifying an initial context-variable value for the context variable detected by the robot at an initial sensory-detection time that corresponds to an initial input time; and
determining the accessed policy based on the initial command and the first context-variable value for the context variable.

4. The method of claim 1 , further comprising:
determining the first value of the robot action variable based on the first context-variable value for the context variable; and
initiating the first robot action in accordance with the first value of the robot action variable.

5. The method of claim 1 , wherein the modifying of the policy further comprises using a learning model.

6. The method of claim 1 , wherein the corrective command is indicative of a magnitude of action.

7. The method of claim 1 , wherein the robot includes the device and further includes a motor used to perform at least part of the first robot action or the second robot action.

8. The method of claim 1 , wherein the user input includes input received at an interface at a user device remote from the robot.

9. A system, comprising:
one or more data processors; and
a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the processor to:
identify a first context-variable value for a context variable detected by a robot at a first sensory-detection time;
access a policy comprising one or more parameters configured to map the context variable to a robot action variable;
determine that a first robot action characterized by a first value of the robot action variable was performed at an action time in response to detection of the first context-variable value, the first robot action being in accordance with application of the policy;
determine that a user input was received at an input time configured to correspond to the action time, wherein user input data derived from the user input at least partly defines a corrective command that specifies a corrective robot action for physical performance, the user input being indicative of at least partial dissatisfaction with the first robot action, wherein the corrective command defined by the user input data is configured to minimize an error associated with the robot action;
modify the policy based on the corrective command and the st context-variable value; and
cause the modified policy to be used to:
determine a second robot action characterized by a second value of the robot action variable based on a second context-variable value for the context variable detected at a second sensory-detection time; and
initiate performance of the second robot action in accordance with the second value of the robot action variable.

10. The system of claim 9 , wherein the instructions further cause the processor to:
identify a third context-variable value for the context variable, the third context-variable value being detected at a third sensory-detection time that is after the second sensory-detection time;
determine that the robot performed a third action in response to the third context-variable value, the third action being in accordance with application of the accessed policy; and
infer that the third action was satisfactory based on a lack of input data least partly defining a corrective command corresponding to the third action;
wherein the modification of the policy is further based on the third context-variable value.

11. The system of claim 9 , wherein the instructions further cause the processor to:
identify initial user input data derived from an initial user input received, the initial user input data at least partly defining an initial command that specifies an initial robot action for the robot to physically perform;
identify an initial context-variable value for the context variable detected by the robot at an initial sensory-detection time that corresponds to the initial input time; and
determine the accessed policy based on the initial command and the first context-variable value for the context variable.

12. The system of claim 9 , wherein the instructions further cause the processor to:
determine the first value of the robot action variable based on the first context-variable value for the context variable; and
initiate the first robot action in accordance with the first value of the robot action variable.

13. The system of claim 9 , wherein the policy is configured to be modified by use of a learning model.

14. The system of claim 9 , wherein the corrective command is indicative of a magnitude of action.

15. The system of claim 9 , wherein the robot includes the system and further includes a motor used to perform at least part of the first robot action or the second robot action.

16. The system of claim 9 , wherein the user input includes input received at an interface at a user device remote from the system.

17. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to:
identify a first context-variable value for a context variable detected by a robo

Assignees

• Brain Corp

Inventors

• Meier Philip
• Passot Jean-Baptiste
• Ibarz Gabardos Borja
• Laurent Patryk
• Sinyavskiy Oleg
• O'Connor Peter
• Izhikevich Eugene

Classifications

• G05B2219/40116

  Learn by operator observation, symbiosis, show, watch · CPC title

• G05B13/027

  using neural networks only · CPC title

• Y10S901/46

  Sensing device · CPC title

• G06N3/008

  based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour · CPC title

• B25J9/1602 Primary

  characterised by the control system, structure, architecture · CPC title