View source

 
For robots to become useful in tasks that involve, interacting and
sharing physical space with humans, they will need to learn to
operate in such a way that they do not disturb or harm people. We
study the problem of developing robots that consider the
<em>affective</em> state of humans during interaction. There are
several questions involved with this problem. The robot has two
possibly conflicting objectives: first, complete a physical task
(such as clean the floor, drill a hole, etc.), and second, do so in
a way that does not disturb the human participant. We study this
problem in the context of a simple tracking task. The physical task
of the robot is to maintian a specified distance from the human.

[[Image:hri.jpg‎|300px]]

We evaluate the "human-friendliness" of a particular controller
by measuring the human's biometric data.  Instead of utilizing
existing controllers designed without regard for the human affective
state, we utilize biofeedback as a means of evaluating the quality
of controllers during the design process. If we can mathematically
relate the comfort level of the human to robot actions, then we can 
form an optimal control problem. Reinforcement learning or the
Calculus of variations can be used to solve such problems. However,
we do not know the relationship between stress response and system
state, and learning the relationship through repeated trials may be
to costly.

[[Image:Gsr.jpg‎|300px]]

Due to the difficulty in modeling the human stress processes, we simplify the problem as follows: for a fixed human trajectory and an initial position of the robot, find a robot trajectory, (rather than a policy over the entire state space) which completes the geometric task while keeping the stress of the human below some maximum value. Our approach to the problem can be described as follows: First compute a set of controllers, each of which is optimal for some objective. The crucial point here is that these objectives do not involve human emotions and therefore corresponding policies can be computed off-line. Our technique involves stitching together portions of the geometric controllers to obtain a human-friendly controller. We repeat the process until a set of admissible trajectories is obtained.

In the tracking task, the robot is charged with maintaining a specified minimum and maximum distance from the human. The goal of the robot is to move so that it maintains the appropriate distance while simultaneously minimizing the measured stress. This task is motivated by scenarios in which the robot may be required to stay close to the human. For example this may be done in order to track the human's gesture. A galvanic skin response (GSR) sensor is used to estimate the stress of the human participant.

[http://www.springerlink.com/content/u31t0h3265443755/fulltext.pdf Paper] In Journal of Autonomous Robots : Special issue on Socially Assistive Robotics 

[http://www.cs.rpi.edu/~meisne/hriposter.pdf Poster]

{{#ev:youtube|g_KSn9M1PAQ}}
{{#ev:youtube|Glqwx4Wf5F0}}
{{#ev:youtube|jdvihfMpaWk}}
{{#ev:youtube|nbl_kS5aqGs}}