A Sketch Interface for Mobile Robots Marjorie Skubic Craig Bailey George Chronis Computational Intelligence Research Lab University of Missouri-Columbia

Outline Motivation and context Route maps The PDA sketch interface Experimental study and results Conclusions and future work

Spatial Reasoning with Guinness References Acknowledgements

Route Maps Tversky’s work Michon and Denis Depictions vs. Descriptions Extraction of route descriptions 1 to 1 correlation Michon and Denis Landmarks and critical nodes

The Sketch Interface Objects Labels Paths Delete Start Move Undo Send

Objects Closed Polygons Any shape or size Thresholds to determine gap closure Feedback on recognition Sound Color

Labels Default numbering for object labels Tap on screen to edit Can use Palm OS Graffiti recognition or a software keyboard

Paths Limit of one A minimum length required Color Feedback

Path Direction Default direction is the direction the path is drawn User can specify the direction with a sketched “blob” to denote the start of the path Recognized by Number of points Average distance of all points Proximity to path endpoint

Delete An intuitive delete: cross out an object Recognized by Two consecutive strokes Both lengths shorter than a path The strokes cross Color feedback Search for closest object or path

Determining Crossed Marks Use the slope equations of lines Endpoints of strokes determine the line A pair of decision parameters can be computed If both parameters lie between 0 and 1, then the two strokes must have an intersection ua= (X4-X3)(Y1-Y3) - (Y4-Y3)(X4-X3) (Y4-Y3)(X2-X1) - (X4-X3)(Y2-Y1) ub= (X2-X1)(Y1-Y3) - (Y2-Y1)(X1-X3) IF (0 < ua < 1) AND (0 < ub < 1) THEN the lines intersect (X1,Y1) (X3,Y3) (X4,Y4) (X2,Y2)

Menu Commands Also accessible through graffiti m  Move u  Undo c  Clear t  Transmit f  Configure

“Digitizing” the Sketch

User Evaluation Tested how well the interface performed with real users Pre-experimental questionnaire Tasks Sketch tasks Re-sketch tasks Task scores Post-experimental questionnaire Questionnaires contain Lickert style statements (Lickert, 1932) along with several open-ended questions

Statistical Analysis 2 groups, 2 scenes: Compared by scene sketched Compared by course level of participant Means compared with the t test Null Hypothesis: there are no differences when compared by sketched scene or course level

Participants 26 students from CS courses One participant scores was not used Only 5 owned a PDA Students of Scene B rated themselves significantly better at giving directions (p = 0.02) No differences when compared by course level

Scene A

Example Sketches of Scene A

Scene B

Example Sketches of Scene B

Post-Experimental Survey: Landmark Scores 1 = very difficult; 5 = very easy Creating Landmarks 4.6 ± 0.6 Deleting Landmarks 4.2 ± 0.9 Usefulness of Deleting 4.7 ± 0.6 Usefulness of Labeling 4.8 ± 0.6

Post-Experimental Survey: Path Scores 1 = very difficult; 5 = very easy Creating a path 4.4 ± 1.0 Deleting a path Usefulness of deleting 4.7 ± 0.7 Usefulness of the starting point 4.2 ± 0.9

Post-Experimental Survey: Overall Scores Usefulness of changing sketch 4.8 ± 0.4 Usefulness of deleting sketch 4.2 ± 1.0 How well sketch represents environment 83.6 ± 7.4 Overall ease of interface 4.4 ± 0.6

Usability Results Only two significant differences (p<=0.05) were found among the scores Usefulness of deleting by scene (p=0.0) Final sketch rating by scene (p=0.05) In both cases, students in scene B rated higher Same group that rated themselves better at giving directions Differences were not found when compared by course level The Null Hypothesis is accepted

Task Score Results Collected sketches were scored +1 for starting landmark +1 for each correct turn +1 for landmark at turn +1 for each correct straight segment + 1 for ending landmark -1 for extra turns or straight segments No significant differences found (p=0.12) Sketch Task Score = 0.91 ± 0.11 Re-sketchTask Score = 0.82 ± 0.26

Conclusions Created a new sketch based interface on a handheld computer Intuitive and little reliance on traditional menus and icons User evaluation finds the interface as easy to use as pencil and paper by 2:1

Future Work Continue integration into the Guinness system Recognition of more sketched symbols Recognition of turning rate Creation of 3D virtual environments with libraries of objects Email: SkubicM@missouri.edu Web: www.cecs.missouri.edu/~skubic funded by the Naval Research Lab

ARCHITECTURE PDA SRserver imageserver speech mapserver spatial behaviors obstacle avoidance pose continuous localization speech robot Cortex sensor data robot cmds corrections oldest short term map user commands and responses SR & map info robot pose encoders sensor info query & label commands sketch directives & feedback robot commands trulla vfh short term map long GUI(EUT) gesture PDA

SRserver User: How many objects do you see? Behind the table User: How many objects do you see? Robot: I am sensing four objects. User: Object 2 is a table. User: Describe the scene. Robot: There are objects on my front right. The object number 4 is mostly in front of me. The table is behind me. User: Go behind the table.

between object 1 and object 2 using the midpoint between closest points using the midpoint between centroids using the CFMD

Image Server

Understanding Sketched Route Maps PATH DESCRIPTION GENERATED FROM THE SKETCHED ROUTE MAP 1. When table is mostly on the right and door is mostly to the rear (and close) Then Move forward 2. When chair is in front or mostly in front Then Turn right 3. When table is mostly on the right and chair is to the left rear Then Move forward 4. When cabinet is mostly in front Then Turn left 5. When ATM is in front or mostly in front Then Move forward 6. When cabinet is mostly to the rear and tree is mostly on the left and ATM is mostly in front Then Stop

References [1] M. Skubic, P. Matsakis, G. Chronis and J. Keller, "Generating Multi-Level Linguistic Spatial Descriptions from Range Sensor Readings Using the Histogram of Forces", Autonomous Robots, Vol. 14, No. 1, Jan., 2003, pp. 51-69. [2] M. Skubic, D. Perzanowski, S. Blisard, A. Schultz, W. Adams, M. Bugajska and D. Brock “Spatial Language for Human-Robot Dialogs,” IEEE Transactions on SMC, Part C, to appear in the special issue on Human-Robot Interaction. [3] M. Skubic, S. Blisard, C. Bailey, J.A. Adams and P. Matsakis, "Qualitative Analysis of Sketched Route Maps: Translating a Sketch into Linguistic Descriptions," IEEE Transactions on SMC Part B, to appear. [4] G. Chronis and M. Skubic, “Sketch-Based Navigation for Mobile Robots,” In Proc. of the IEEE 2003 Intl. Conf. on Fuzzy Systems, May, 2003, St. Louis, MO. [5] G. Scott, J.M. Keller, M. Skubic and R.H. Luke III, “Face Recognition for Homeland Security: A Computational Intelligence Approach,” In Proc. of the IEEE 2003 Intl. Conf. on Fuzzy Systems, May, 2003, St. Louis, MO.

Guinness and Gang From left to right George Chronis, Grant Scott, Dr. Marge Skubic, Matt Williams, Craig Bailey, Bob Luke, Charlie Huggard and Sam Blisard Missing: Dr. Jim Keller

Sketch-Based Navigation The robot traversing the sketched route The sketched route map

Sketch-Based Navigation The robot traversing the sketched route The digitized sketched route map

Acknowledgements This work has been supported by ONR and the U.S. Naval Research Lab. Natural language understanding is accomplished using a system developed by NRL, called Nautilus [Wauchope, 2000]. We also want to acknowledge the help of Dr. Pascal Matsakis.

NRL’s Multimodal Robot Interface