Trainable modular robotic apparatus

• US 10,166,675 B2
• Filed: 11/19/2015
• Issued: 01/01/2019
• Est. Priority Date: 03/13/2014
• Status: Active Grant

• Current Assignee: Brain Corporation
• Original Assignee: Brain Corporation
• Inventors: Izhikevich, Eugene, Fisher, Dimitry, Passot, Jean-Baptiste, Hatcher, Heathcliff, Polonichko, Vadim
• Primary Examiner(s): Rink, Ryan
• Application Number:
  US14/946,589
• Publication Number:
  US 20160075018A1
• Time in Patent Office: 1,139 Days
• Field of Search: None
• CPC Class Codes: A63H 3/20 with parts moved due to movements of other parts, e.g. limbs
• CPC Class Codes: B25J 13/08 by means of sensing devices, e.g. viewing or touching devices
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B25J 9/1694 characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/008 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 Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: Y10S 901/02 Arm motion controller
• CPC Class Codes: Y10S 901/04 Manual lead through
• CPC Class Codes: Y10S 901/09 Closed loop, sensor feedback controls arm movement
• CPC Class Codes: Y10S 901/50 Miscellaneous

Abstract

Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.

Claims

1. A robotic apparatus operable to conduct one or more assigned physical tasks, the robotic apparatus comprising:

at least one processor; and

a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being configured to cause, when executed by the at least one processor, a first controller of the robotic apparatus to;

produce an output signal based on a sensory input comprising an environmental characteristic and a learning process of the first controller, the first controller being configured to be coupled to a first robotic body of a plurality of robotic bodies via a first type of interface, the first robotic body including a first set of one or more degrees of freedom;

initialize a trajectory of the first robotic body of the plurality of robotic bodies based on the output signal;

receive a teaching signal in response to the initialized trajectory that teaches a target trajectory;

update the learning process of the first controller based on the received teaching signal; and

implement an adjusted trajectory for the first robotic body of the plurality of robotic bodies based on the updated learning process of the first controller;

store the adjusted trajectory in one or more trained robotic configurations; and

transfer the stored one or more trained robotic configurations to a second controller on a second robotic body of the plurality of robotic bodies via a second type of interface, the second robotic body including a second set of one or more degrees of freedom distinct from the first set of one or more degrees of freedom, the second type of interface being different from the first type of interface, the one or more trained robotic configurations comprising the updated learning process of the first controller;

wherein the transfer of the stored one or more trained robotic configurations to the second controller is configured to;

enable an implementation of the adjusted trajectory on the second robotic body of the plurality of robotic bodies via the transfer, the implementation of the adjusted trajectory on the second robotic body of the plurality of robotic bodies being based on the transferred one or more trained robotic configurations; and

enable the second controller of the second robotic body of the plurality of robotic bodies to update the updated learning process for use by one or more other controllers other than the second controller.

2. The robotic apparatus of claim 1, wherein the first controller comprises a learning apparatus capable of being trained to conduct the one or more assigned physical tasks via at least feedback, the feedback comprising the teaching signal;

wherein the robotic apparatus is configured to train the learning apparatus to conduct the one or more assigned physical tasks via the at least feedback.

3. The robotic apparatus of claim 1, wherein the first controller is in data communication with one or more motive sources, and is configured to produce one or more inputs, the one or more inputs comprising one or more mechanical force inputs.

4. The robotic apparatus of claim 1, wherein:

the at least one processor is further configured to operate an adaptive learning process to conduct the one or more assigned physical tasks, the adaptive learning process being characterized by a plurality of trials;

the robotic apparatus is configured to provide at least one actuation output to the first robotic body, the at least one actuation output comprising first and second portions configured to effectuate movement of a first controllable element and a second controllable element of the first robotic body, respectively;

the adaptive learning process is configured to determine, during a trial of the plurality of trials, at least one actuation output, the at least one actuation output having a first trajectory associated therewith;

the first controllable element is configured to effectuate movement of the first robotic body in a first degree of freedom (DOF);

the second controllable element is configured to effectuate movement of the first robotic body in a second DOF independent from the first DOF; and

the first and the second portions of the at least one actuation output are configured based on one or more instructions from the at least one processor.

5. The robotic apparatus of claim 4, wherein:

the movement in the first DOF of the first controllable element and the movement in the second DOF of the second controllable element cooperate to effectuate conduction of the one or more assigned tasks by the first robotic body;

the adaptive learning process comprises a haptic learning process characterized by at least a teaching input provided by a trainer; and

the teaching input is configured based on an adjustment of the first trajectory via a physical contact of the trainer with the first robotic body.

6. The robotic apparatus of claim 5, wherein:

the adjustment of the first trajectory is configured based at least on an observation of a discrepancy between the first trajectory and the target trajectory during the trial; and

the adjustment of the first trajectory is configured to cause a modification of the learning process so as to generate the updated learning process and determine a second control input configured to transition the first trajectory towards the target trajectory during another trial subsequent to the trial.

7. The robotic apparatus of claim 6, wherein:

the modification of the learning process is characterized by a determination of one or more values by the at least one processor;

the robotic apparatus is further configured to provide another actuation output to the second robotic body, the other actuation output being configured to effectuate movement of the second robotic body in a first DOF; and

the other actuation output is configured based on the one or more values.

8. The robotic apparatus of claim 4, wherein:

a detachable enclosure is configured to house a camera adapted to provide sensory input to the at least one processor, the sensory input being used for determination of the at least one actuation output in accordance with a target task, and

the one or more instructions are configured based on at least the sensory input.

9. The robotic apparatus of claim 4, wherein:

the at least one processor is configured to receive an audio input, the audio input being used for determining the at least one actuation output in accordance with a target task, and

the one or more instructions are configured based on at least the audio input.

10. The robotic apparatus of claim 9, wherein:

a detachable enclosure is configured to house a sound receiving module configured to effectuate provision of the audio input to the processor; and

the audio input is configured based at least on a command of a trainer.

11. The robotic apparatus of claim 4, wherein:

a detachable enclosure is configured to house one or more inertial sensors configured to provide information related to a movement characteristic of the first robotic body to the at least one processor; and

the one or more instructions are configured based at least on the information.

12. The robotic apparatus of claim 4, wherein the at least one processor is configured to determine a displacement of a first joint and a second joint associated, respectively, with the movement of the first controllable element in the first degree of freedom (DOF) and the movement of the second controllable element in the second DOF.

13. The robotic apparatus of claim 12, wherein the determination of the displacement is configured based at least on feedback information provided from first and second actuators to the at least one processor, and the feedback information comprises one or more of actuator displacement, actuator torque, and/or actuator current draw.

14. The robotic apparatus of claim 4, wherein:

the first robotic body comprises an identifier configured to convey information related to a configuration of the first robotic body; and

the adaptive learning process is configured to adapt a parameter based on receipt of the information, the adaptation of the parameter being configured to enable the adaptive learning process to adapt the at least one actuation output consistent with the configuration of the first robotic body.

15. The robotic apparatus of claim 1, wherein the first type of interface comprises:

a first interface portion comprising a shape; and

a second interface portion particularly adapted to interface only with other interface portions comprising the shape;

wherein a coupling of the first and second interface portions is configured to animate the first robotic body via at least a mechanical force transferred over the coupled first and second interface portions.

16. The robotic apparatus of claim 15, wherein the coupled first and second interface portions comprise at least one mechanical interface configured to transfer a force, and at least one electrical interface configured to transfer electrical signals or power across the coupled first and second interface portions;

and

the shape comprises a substantially male feature.

17. The robotic apparatus of claim 1, wherein the transfer of the one or more trained robotic configurations by the first controller of the robotic apparatus via the second type of interface comprises (i) storage of one or more neural network weights determined by the learning process on a server entity and (ii) transfer of the one or more neural network weights from the server entity to the second robotic body or a third robotic body.

18. The robotic apparatus of claim 17, wherein:

the storage of the one or more neural network weights comprises;

addition of the one or more trained configurations to a repository of trained configurations; and

addition of performance metrics corresponding to the one or more trained configurations; and

the one or more trained configurations are configured to be selectable among the repository of trained configurations by a user of the second robotic body or a user of the third robotic body.

19. The robotic apparatus of claim 1, wherein the received teaching signal is determined based on a first discrepancy measure between the trajectory of the first robotic body and the target trajectory;

wherein the instructions are further configured to cause, when executed by the at least one processor, the first controller of the robotic apparatus to initialize the adjusted trajectory of the robotic body based on the updated learning process of the first controller, the adjusted trajectory comprising a second discrepancy measure that is smaller than the first discrepancy measure, the second discrepancy measure being between the adjusted trajectory of the first robotic body and the target trajectory.

20. The robotic apparatus of claim 1, wherein the instructions are further configured to cause, when executed by the at least one processor, the first controller of the robotic apparatus to receive a concurrent teaching indication that accompanies the teaching signal.