It is often difficult to predict the high-level behavior of a robot given low-level models about sensors, actuators and controllers. You might know your robot will turn in response to obstacles but not how it will behave in a room full of people.

Modeling the global behavior of a robot is useful in order to predict how the robot behaves in different environments. Furthermore, once a good model is inferred, it can be used to improve the robot’s controller parameters online.

To model robot behaviors, Infantes et al. use a probabilistic representation called Dynamic Bayesian Networks. The approach is tested using the Rackham RWI B21R museum guide robot shown below that needs to navigate in an open environment with people. The network captures information concerning the robot’s parameters, environment variables, robot state variables and mission variables. The model is then used to optimize the robot behavior for a given environment. During the learning process, robots are rewarded for good behaviors that avoid failures, go fast and are “human-friendly”. Using this approach, the robot fails less, is faster and has better human acceptance than a robot with hand-tuned parameters.

In the future, Infantes et al. plan to use this approach to learn other robotic tasks such as grasping or interacting with humans.