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Learning behavioral models

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 […]

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.

By Sabine Hauert

Sabine Hauert is Assistant Professor in Robotics at the University of Bristol in the UK. Her research focusses in designing swarms that work in large numbers (>1000), and at small scales (<1 cm). Profoundly cross-disciplinary, Sabine works between Engineering Mathematics, the Bristol Robotics Laboratory, and Life Sciences. Before joining the University of Bristol, Sabine engineered swarms of nanoparticles for cancer treatment at MIT, and deployed swarms of flying robots at EPFL.

Sabine is also President and Co-founder of Robohub.org, a non-profit dedicated to connecting the robotics community to the world.

As an expert in science communication with 10 years of experience, Sabine is often invited to discuss the future of robotics and AI, including in the journals Nature and Science, at the European Parliament, and at the Royal Society. Her work has been featured in mainstream media including BBC, CNN, The Guardian, The Economist, TEDx, WIRED, and New Scientist.