World Modeling by Manipulation

T. Debus*, P. Dupont*, R. Howe**
*Robotics, Dynamics and Control research group, Boston University
**Biorobotics Laboratory, Harvard University


Support provided by The National Science Foundation

 

 

MOTIVATION  
Teleoperation is the ideal method to perform sophisticated tasks in unknown environments. In this control mode, all the operator’s actions are based on his/her interpretation of the camera images. In many cases, however, humans are not good at quantification and cannot interpret this visual information in an exact manner, prohibiting them from designing the correct strategy.  
   

 

PROPOSED SOLUTION
The proposed solution is to create a modeling system that will use sensor information (i.e., machine perception) used for control purposes to determine the properties of the objects and environment as the task progresses. One practical application is a teleoperator assistant that can provide information to the operator in real time during task execution. This includes quantitative measurements such as the size, shape, and orientation of objects; these properties can, for example, help determine appropriate insertion strategies in teleoperated assembly task.
   

 

Algorithm used to Automatically Identify Object Properties

 

To automatically identify object properties, two basics and essential
concepts are used:
1) every task can be described as a network of states
2) properties are only measurable in certain states. Based on these two concepts, a framework has been developed to reduce the overall problem to three sub-problems:

Task decomposition: The operator decomposes the task into a minimal sequence of subtasks characterized by their contact states geometry and associated sets of properties.

Data segmentation: Given a task decomposition and the sensor data stream, find the time intervals corresponding to each subtask.

Property estimation: Given the time intervals associated with each subtask, estimate the desired properties.

   

 

The Mathematical Approach: Constraint Equations and Multiple Model Estimation
In order to stay in contact with the environment the robot’s wrist has to be constrained. Moreover, how the wrist is constrained can be a unique characterization of the contact state. This can be modeled thru physical models using constraint equations parameterized by sensing data and object properties.
Once a mathematical model is built for each contact state, a multiple regression algorithm is used to estimate all the properties associated with all the contact states. Based on the quality of the estimates an acceptance test decides if a contact is active or not. Once we know which contact is active we can keep the associated estimated properties.
   

 

To get more detailed information on the techniques used to automatically identify remote object properties, here is a list of references.

 

REFERENCES

T. Debus, P. Dupont and R.D. Howe (2002).
"Contact State Estimation Using Multiple Model Estimation and Hidden Markov Model," in B. Siciliano and P. Dario, Eds., Experimental Robotics VIII. The Eigth Internation Symposium. Lecture notes in control and information sciences, Springer-Verlag, Ischia, Italy, July 2002.

T. Debus, P. Dupont and R.D. Howe (2000).
"Automatic Indentification of Local Geometric Properties During Teleoperation," Proceedings of the IEEE International Conference on Robotics & Automation, San Francisco, April 2000, pp. 3428-3434.

T. Debus, J. Stoll, R. D. Howe, and P. Dupont (2000).
"Cooperative Human and Machine Perception in Teleoperated Assembly," in D. Rus and S. Singh, Eds., Experimental Robotics VII. The Seventh International Symposium. Lecture notes in control and information sciences, Springer-Verlag. Honolulu, HI, December 2000.

T. Debus, P. Dupont and R.D. Howe (1999).
"Automatic Property Identification via Parameterized Constraints," Proceedings of the IEEE International Conference on Robotics & Automation, Detroit, May 1999, pp. 1876-81.

P. Dupont, T. Schulteis, P. Millman, and R. D. Howe (1999).
"Automatic Identification of Environment Haptic Properties," Presence 8(4), pp. 392-409, August 1999.