1. Teaching Robot’s Proactive Behavior Using Human Assistance
Garrell, M. Villamizar, F. Moreno-Noguer, A. Sanfeliu
Presentation by Geffen Huberman

2. Objective
Introduce a framework to initiate a proactive, natural-seeming engagement with a human
Introduce a framework in which a robot can receive human assistance for on-line face detection learning

3. Our Robot: TIBI Obstacles:
Mimicking human-like navigation: how should Tibi navigate space?
Mimicking natural human conversation initiation: How should Tibi approach identified people?
Modeling human emotion: how should Tibi react to other people

4. The Protocol: Fleshed Out in DFAs

5. Question #1: How Should Tibi Navigate Space?
Weighted sum of forces
Ferrer G, Garrell A, Sanfeliu A (2013) Robot companion: a social-force based approach with human awareness-navigation in crowded environments. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp 1688–1694
“Extended Social Force Model”
Tibi’s Max Velocity Based on Proximity of Forces

6. Breakdown of Different Forces:
Attractive force to chosen assistant
Desired velocity
Actual velocity
Relaxation time
Summed repulsive forces from pedestrians
Summed repulsive forces from obstacles

7. Question #2: How Should Tibi Approach?
Invitation to Begin Interaction:
Hello, I am Tibi. I’m trying to learn to detect faces, could you help me?
Hi, I am Tibi, I would like to learn to recognize different objects, will you be my teacher?
Invitation to Continue Interaction:
It will take just 2 min
Please, don’t go
Let me explain you first the goal of this experiment, and then, you can decide if you want to stay
Three Robot Behaviors:
Verbal communication
Gestures
Robot motions/movement (moving closer/farther away)
Michalowski M, Sabanovic S, Simmons R (2006) A spatial model of engagement for a social robot. In: Proceedings of the 9th IEEE international workshop on advanced motion control, pp 762–767

8. Question #3: How Should Tibi Respond - The Emotional Model
Synthesize emotions like happiness, sadness, anger, frustration, etc. based on three factors corresponding to physiological indicators:
Stance: open vs. closed
Valence: negative vs. positive
Arousal: low vs. high
Choices:
Stance: open
Valence: increases with the accomplishment of a goal [-1, 1] Different goals are associated with different levels of achievement.
Arousal: determined by pre-defined proxemics
Schlosberg H (1954) Three dimensions of emotion. Psychol Rev 61(2):81

9. Arousal Social and personal zones Intimate zone Public zone
Intensity determined by ALL people around Tibi; if there is ‘personal’ distance but not ‘intimate’ distance, it will also consider the change in intensity (i.e. looking at the group of people at a previous time); at intimate distance, it will look at all of the distances in that perceived space, 0 for public distances
If a social distance has been penetrated, the arousal will be determined by the change in intensity for all of the people having given that intensity; otherwise, the arousal is the change in the intensity over time.
Arousal

10. Putting the Emotional Model Together
Human establishes intimate proximity and robot accomplishes goals
Human comes closer but does is unresponsive to robot’s requests
No goals accomplished, human maintains distance
Human slowly retreats as robot is continuously unsuccessful in its goals

11. Face Detection Classifier
Classifier based on Online Random Ferns
Robot provides the human with a wii controller: allows human to respond to claims of accurate face detection by Tibi
Accurate face detection: classifier > 0.5; inaccurate: classifier < 0.5
Confidence interval when classifier is 0.5: requires human intervention (θ)
Hmm… uncertain
Correct ID
The Random Fern classifier itself is composed of random and simple binary features based on pixel intensity of RGB values
The Online Random Fern classifier uses the same principle. It first uses offline learning to learn faces, and then for the online detection has a “bootstrapping” step in which it uses the human assistance to then resample the previous positive and negative classifications that it had
Villamizar M, Garrell A, Sanfeliu A, Moreno-Noguer F (2012) Online human-assisted learning using random ferns. In: Proceedings of the 21st international conference on pattern recognition, pp 2821–2824

12. Putting Tibi Into Action
Tested on 4 different outdoor areas in the UPC campus with 50 different people with no prior experience with robots
Randomly activated one of the three robot behaviors before asking for assistance
B1: Only verbal communication
B2: Verbal communication and gestures
B3: Verbal + nonverbal communication, approaches person

13. The Protocol
Fourth component: Feedback from experiment participants on social normalcy of the robot’s behavior