Trainable modular robotic apparatus and methods

• US 9,533,413 B2
• Filed: 03/13/2014
• Issued: 01/03/2017
• Est. Priority Date: 03/13/2014
• Status: Expired due to Fees

• Current Assignee: Brain Corporation
• Original Assignee: Brain Corporation
• Inventors: Izhikevich, Eugene, Fisher, Dimitry, Passot, Jean-Baptiste, Hatcher, Heathcliff, Polonichko, Vadim
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): RINK, RYAN J
• Application Number:
  US14/209,826
• Publication Number:
  US 20150258683A1
• Time in Patent Office: 1,027 Days
• Field of Search: 446/330
• US Class Current: 1/1
• 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 learning controller apparatus configured to operate a robotic body, the learning controller apparatus comprising:

a processor configured to operate an adaptive learning process in order to execute a task, the adaptive learning process being characterized by a plurality of trials;

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

a first actuator and a second actuator each in operable communication with the processor, the first and the second actuators being configured to provide the first and the second portions of the at least one actuation output, respectively;

wherein;

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

the adaptive learning process is further configured to determine, during a subsequent trial of the plurality of trials, an other actuation output having a second trajectory associated therewith, the second trajectory being closer to a target trajectory of the task than the first trajectory;

the 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;

the first and second interface portions are mated together and are configured to animate the robotic body via at least a mechanical force transferred over the mated first and second interface portions; and

where at least one electrical interface is configured to transfer electrical signals or power across the mated first and second interface portions, and the shape comprises a substantially male feature.

2. The learning controller apparatus of claim 1, wherein:

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

the second controllable element is configured to effectuate movement of the 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 processor.

3. The learning controller apparatus of claim 2, wherein:

the operation of the adaptive learning process by the processor is configured based on one or more computer-executable instructions; and

the processor is configured to upload the one or more computer-executable instructions to a computer readable medium disposed external to an enclosure of the learning controller apparatus.

4. The learning controller apparatus of claim 2, 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 execution of the task by the 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 robotic body.

5. The learning controller apparatus of claim 4, 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 further configured to cause a modification of the learning process so as to determine a second control input configured to transition the first trajectory towards the target trajectory during another trial subsequent to the trial.

6. The learning controller apparatus of claim 5, wherein:

the modification of the learning process is characterized by a determination of one or more values by the processor,

the learning controller apparatus is configured to provide the other actuation output to another robotic body, the other actuation output being configured to effectuate movement of the other robotic body in a third DOF; and

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

7. The learning controller apparatus of claim 2, wherein:

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

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

8. The learning controller apparatus of claim 2, wherein:

the processor is configured to receive an audio input, the at least one actuation output in accordance with a target task determined based at least in part on the audio input, and

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

9. The learning controller apparatus of claim 8, further comprising:

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

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

10. The learning controller apparatus of claim 2, wherein:

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

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

11. The learning controller apparatus of claim 1, wherein the 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 a first degree of freedom (DOF) and the movement of the second controllable element in a second DOF.

12. The learning controller apparatus of claim 11, wherein the determination of the displacement is configured based at least on feedback information provided from the first and the second actuators to the processor, and the feedback information comprises one or more of an actuator displacement, an actuator torque, and an actuator current draw.

13. The learning controller apparatus of claim 1, wherein:

the robotic body is configured to convey information related to a configuration of the 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 robotic body.

14. The learning controller apparatus of claim 13, wherein:

the information related to the configuration of the robotic body comprises a number of joints of the robotic body, and a number and an identification of degrees of freedom of the joints of the robotic body;

the actuation output configured consistent with the configuration of the robotic body comprises an output configured to operate one or more of the joints of the robotic body in a respective degree of freedom; and

the adaptation of the parameter is configured based on one or more instructions executed by the processor, the one or more instructions being related to two or more of the degrees of freedom of the joints of the robotic body.

15. The learning controller apparatus of claim 1, wherein the second interface portion comprises a substantially female feature, the substantially male and substantially female features being configured to rigidly but separably attach to one other.

16. A method of operating a robotic body with a controller apparatus, the method comprising:

operating an adaptive learning process to execute a task, the adaptive learning process comprising a plurality of trials;

providing at least one actuation output to the robotic body, the at least one actuation output comprising a first portion and a second portion each enabling movement of a first controllable element and a second controllable element, respectively, of the robotic body;

providing the first portion and the second portion of the at least one actuation output;

during a trial of the plurality of trials, determining the at least one actuation output to the robotic body, the at least one actuation output comprising a first trajectory associated therewith;

during a subsequent trial of the plurality of trials, determining an other actuation output comprising a second trajectory associated therewith, the second trajectory being closer to a target trajectory of the task than the first trajectory; and

mating together a first portion of an interface and a second portion of the interface, and animating the robotic body via at least a mechanical force transferred over the mated first and second portions of the interface;

wherein;

the first portion of the interface comprises a shape, and the second portion of the interface is particularly adapted to interface only with other interface portions comprising the shape; and

the mated first and second portions of the interface comprise at least one mechanical interface configured to transfer a force, and at least one electrical interface is configured to transfer electrical signals or power across the mated first and second portions of the interface.

17. The method of claim 16, wherein:

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

the second controllable element is configured to effectuate movement of the 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 processor.

18. The method of claim 17, wherein:

the movement in the first DOF of the first controllable element and the movement in the second DOF of the second controllable element are configured to cooperate to effectuate the execution of the task;

the adaptive learning process further 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 robotic body.

19. The method of claim 18, 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 further configured to modify the learning process so as to determine a second control input configured to transition the first trajectory toward the target trajectory during another trial subsequent to the trial.

20. A non-transitory computer-readable storage medium comprising a plurality of instructions, the plurality of instructions being configured to, when executed by a processor, cause an apparatus configured to operate a robotic body to:

operate an adaptive learning process to execute a task, the adaptive learning process comprising a plurality of trials;

provide at least one actuation output to the robotic body, the at least one actuation output comprising a first portion and a second portion each enabling movement of a first controllable element and a second controllable element, respectively, of the robotic body;

provide the first portion and the second portion of the at least one actuation output;

determine the at least one actuation output to the robotic body during a trial of the plurality of trials, the at least one actuation output comprising a first trajectory associated therewith;

determine another actuation output comprising a second trajectory associated therewith during a subsequent trial of the plurality of trials, the second trajectory being closer to a target trajectory of the task than the first trajectory;

mate together a first portion of an interface and a second portion of the interface; and

animate the robotic body via at least a mechanical force transferred over the mated first and second portions of the interface;

wherein;

the first portion of the interface comprises a shape, and the second portion of the interface is particularly adapted to interface only with other interface portions comprising the shape; and

the mated first and second portions of the interface comprise at least one mechanical interface configured to transfer a force, and at least one electrical interface is configured to transfer electrical signals or power across the mated first and second portions of the interface.

21. The non-transitory computer-readable storage medium of claim 20, wherein the plurality of instructions are further configured to, when executed by the processor, cause the apparatus configured to operate the robotic body to:

determine a displacement of a first joint and a second joint associated, respectively, with the movement of the first controllable element in a first degree of freedom (DOF) and the movement of the second controllable element in a second DOF.

22. The non-transitory computer-readable storage medium of claim 20, wherein the plurality of instructions are further configured to, when executed by the processor, cause the apparatus configured to operate the robotic body to:

produce one or more inputs and communicating the one or more inputs to enable the robotic body to execute the task using at least one degree of freedom.

23. The non-transitory computer-readable storage medium of claim 22, wherein the one or more inputs comprise one or more mechanical force inputs via one or more motive sources.