Machine Learning Approach for Predicting Humanoid Robot Fall

• US 20100292838A1
• Filed: 01/29/2010
• Published: 11/18/2010
• Est. Priority Date: 05/15/2009
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Kalyanakrishnan, Shivaram, Goswami, Ambarish
• Granted Patent: US 8,554,370 B2
• Time in Patent Office: Days
• US Class Current: 700/246
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B62D 57/032 with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid B62D57/024 takes precedence
• 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

Abstract

A system and method is disclosed for predicting a fall of a robot having at least two legs. A learned representation, such as a decision list, generated by a supervised learning algorithm is received. This learned representation may have been generated based on trajectories of a simulated robot when various forces are applied to the simulated robot. The learned representation takes as inputs a plurality of features of the robot and outputs a classification indicating whether the current state of the robot is balanced or falling. A plurality of features of the current state of the robot, such as the height of the center of mass of the robot, are determined based on current values of a joint angle or joint velocity of the robot. The current state of the robot is classified as being either balanced or falling by evaluating the learned representation with the plurality of features of the current state of the robot.

Claims

1. A method for predicting a fall of a robot having at least two legs, the method comprising:

receiving a learned representation generated by a supervised learning algorithm, the learned representation taking as inputs a plurality of features of a robot, the learned representation having as an output a classification comprising an indication of a balanced state or a falling state;

determining a plurality of features of a current state of the robot, the determining based at least in part on a current value of a joint angle or joint velocity of the robot; and

classifying the current state of the robot as being either balanced or falling, the classifying performed by evaluating the learned representation with the plurality of features of the current state of the robot.

2. The method of claim 1, further comprising:

generating the learned representation using the supervised learning algorithm.

3. The method of claim 2, wherein generating the learned representation using the supervised learning algorithm further comprises:

applying a plurality of simulated force impulses to a simulation of the robot, the force impulses varying in magnitude of force and direction of application;

recording a plurality of trajectories generated from the motions of the robot after application of the plurality of simulated force impulses, each trajectory comprising a plurality of instances, each instance comprising a plurality of features describing the state of the robot at the particular instance.

classifying each instance as a balanced instance or as a falling instance based on whether the trajectory containing the instance ends in a fallen state; and

processing the features and classification of each instance with the supervised learning algorithm to generate the learned representation.

4. The method of claim 3, wherein generating the learned representation using the supervised learning algorithm further comprises:

removing an instance classified as falling if the instance occurs before a specific time in a trajectory containing the instance, wherein the specific time is based on a time of onset of a monotonic decrease of a height of a center of mass of the robot.

5. The method of claim 1, wherein the learned representation comprises a decision list.

6. The method of claim 1, further comprising:

responsive to classifying the current state of the robot as being falling, activating a fall controller of the robot.

7. The method of claim 6, wherein the fall controller of the robot controls the robot to fall in a manner that reduces damage to the robot or an object near the robot.

8. The method of claim 1, wherein one of the plurality of features comprises a height of the center of mass of the robot.

9. The method of claim 1, wherein one of the plurality of features comprises a foot contact mode of the robot.

10. The method of claim 9, wherein the learned representation comprises a plurality of decision lists each associated with a different foot contact mode of the robot.

11. A system for predicting a fall of a robot having at least two legs, the system comprising:

a computer-readable storage medium storing executable computer program modules configured for;

receiving a learned representation generated by a supervised learning algorithm, the learned representation taking as inputs a plurality of features of a robot, the learned representation having as an output a classification comprising an indication of a balanced state or a falling state;

determining a plurality of features of a current state of the robot, the determining based at least in part on a current value of a joint angle or joint velocity of the robot; and

classifying the current state of the robot as being either balanced or falling, the classifying performed by evaluating the learned representation with the plurality of features of the current state of the robot.

12. The system of claim 11, wherein the modules are further configured for:

generating the learned representation using the supervised learning algorithm.

13. The system of claim 12, wherein generating the learned representation using the supervised learning algorithm further comprises:

applying a plurality of simulated force impulses to a simulation of the robot, the force impulses varying in magnitude of force and direction of application;

recording a plurality of trajectories generated from the motions of the robot after application of the plurality of simulated force impulses, each trajectory comprising a plurality of instances, each instance comprising a plurality of features describing the state of the robot at the particular instance.

classifying each instance as a balanced instance or as a falling instance based on whether the trajectory containing the instance ends in a fallen state; and

processing the features and classification of each instance with the supervised learning algorithm to generate the learned representation.

14. The system of claim 13, wherein generating the learned representation using the supervised learning algorithm further comprises:

removing an instance classified as falling if the instance occurs before a specific time in a trajectory containing the instance, wherein the specific time is based on a time of onset of a monotonic decrease of a height of a center of mass of the robot.

15. The system of claim 11, wherein the learned representation comprises a decision list.

16. The system of claim 11, wherein the modules are further configured for:

responsive to classifying the current state of the robot as being falling, activating a fall controller of the robot.

17. The system of claim 16, wherein the fall controller of the robot controls the robot to fall in a manner that reduces damage to the robot or an object near the robot.

18. The system of claim 11, wherein one of the plurality of features comprises a height of the center of mass of the robot.

19. The system of claim 11, wherein one of the plurality of features comprises a foot contact mode of the robot.

20. The system of claim 19, wherein the learned representation comprises a plurality of decision lists each associated with a different foot contact mode of the robot.