Sensor
information is provided to the
algorithms responsible for estimating self- motion and interaction with the world. Robots can be programmed with their own versions of mental models, com- plete with mechanisms for learning and adaptation that help encode knowledge about themselves and the environment in which they operate. Rather than “mental models,” we call these “world models.”
‘IN FORM AND MOVING HOW EXPRESS AND ADMIRABLE’ Consider a robot acting while assuming a model of its own motion in the world. If the behavior the robot actually expe- riences deviates significantly from the behavior the robot expects, the discrep- ancy will lead to poor performance: a
“wobbly” robot that is slow and confused, not unlike a human after too many alco- holic beverages. If the actual motion is closer to the anticipated model, the robot can be very quick and accurate with less burden on the sensing aspect to correct for erroneous modeling.
Of course, the environment itself greatly affects how the robot moves through the world. While gravity can fortunately be assumed constant on Earth, other con- ditions can change how a robot might interact with the environment. For instance, a robot traveling through mud would have a much different experi- ence than one moving on asphalt. Te best modeling would be designed to change depending on the environment. We know there are many models to be learned and applied, and the real issue is knowing which model to apply for a given situation.
Robotics today are developed in labora- tory environments with little exposure to the variability of the world outside the lab, which can cause a robot’s abil- ity to perceive and react to fail in the
unstructured outdoors. Limited envi- ronmental exposure during model learning and subsequent poor adapta- tion or performance is said to be the result of “over-fitting,” or using a model created from a small subset of experi- ences to maneuver according to a much broader set of experiences.
CONCLUSION At ARL, we are researching specific advances to address these areas of sens- ing, modeling self-motion and modeling robotic interaction with the world, with the understanding that doing so will enable great enhancements in the opera- tional speed of autonomous vehicles.
Specifically, we are working on knowing when and under what conditions different methods of sensing work well or may not work well. Given this knowledge, we can balance how these sensors are combined to aid the robot’s motion estimation.
A much faster estimate is available as well through development of techniques to automatically estimate accurate models of
the world and of robot
WIRED FOR DISCOVERY Earl Jared Shamwell, one of the authors, sets up a multisensor robotics test bed to collect images, light detection and ranging data and inertial measurements. Researchers aim to improve robotic performance by closing the gap between what a robot expects to happen and what actually happens. (Photo by Jhi Scott, ARL)
self-motion.
With the learned and applied models, the robot can act and plan on a much quicker timescale than what might be possible with only direct sensor measurements.
Finally, we know that these models of motion should change depending on which of the many diverse environmen- tal conditions the robot finds itself
in.
To further enhance robot reliability in a more general sense, we are working on how to best model the world such that a collection of knowledge can be leveraged to help select an appropriate model of robot motion for the current conditions.
If we can master these capabilities, then Rosie can be ready for operation, lacking only her signature attitude.
For more information about ARL col- laboration opportunities in the science for maneuver, go to
http://www.arl.army. mil/opencampus/.
DR. JOSEPH CONROY is an electronics engineer in ARL’s Micro
and Nano
Materials and Devices Branch, Adelphi, Maryland. He holds a doctorate, an M.S. and a B.S., all in aerospace engineering and all from the University of Maryland, College Park.
MR. EARL JARED SHAMWELL is a systems engineer with General Technical Services LLC, providing contract support to ARL’s Micro and Nano Materials and Devices Branch. He is working on his doc- torate in neuroscience from the University of Maryland, College Park, and holds a B.A. in economics and philosophy from Colum- bia University.
ASC.ARMY.MIL 105
SCIENCE & TECHNOLOGY
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